Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
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paper_title: Causal Relation Extraction

paper_content:

This paper presents a supervised method for the detection and extraction of Causal Relations from open domain text. First we give a brief outline of the definition of causation and how it relates to other Semantic Relations, as well as a characterization of their encoding. In this work, we only consider marked and explicit causations. Our approach first identifies the syntactic patterns that may encode a causation, then we use Machine Learning techniques to decide whether or not a pattern instance encodes a causation. We focus on the most productive pattern, a verb phrase followed by a relator and a clause, and its reverse version, a relator followed by a clause and a verb phrase. As relators we consider the words as, after, because and since. We present a set of lexical, syntactic and semantic features for the classification task, their rationale and some examples. The results obtained are discussed and the errors analyzed.
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paper_title: Models of causation and the semantics of causal verbs

paper_content:

Abstract This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation ( Cheng, 1997 ; Cheng and Novick (1991 , Cheng and Novick (1992 ), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on Talmy’s (1988) theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.
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paper_title: Learning Causality for News Events Prediction

paper_content:

The problem we tackle in this work is, given a present news event, to generate a plausible future event that can be caused by the given event. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precise labeled causality examples, we mine 150 years of news articles, and apply semantic natural language modeling techniques to titles containing certain predefined causality patterns. For generalization, the model uses a vast amount of world knowledge ontologies mined from LinkedData, containing ~200 datasets with approximately 20 billion relations. Empirical evaluation on real news articles shows that our Pundit algorithm reaches a human-level performance.
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paper_title: Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

paper_content:

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture! exacerbate desertification from the web using semantic relation (between nouns), context, and association features. Experiments show that our method outperforms baselines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture! exacerbate desertification! increase Asian dust (from China)! asthma gets worse. Experiments show that we can generate 50,000 scenarios with 68% precision. We also generated a scenario deforestation continues! global warming worsens! sea temperatures rise! vibrio parahaemolyticus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we “predicted” the future event sequence in a sense.
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paper_title: Semantic relations across syntactic levels

paper_content:

In order to make sense of a message conveyed to us via a spoken or written utterance, we understand what things are talked about, and how they are connected. ::: From this point of view, do these sentences convey different messages? (1) I will arrive at 11 am. and I will arrive when you arrive. (2) I will meet you in the office. and I will meet you where we met last time. (3) Sweets before dinner spoil your appetite. and (4) Eating sweets before dinner spoils your appetite. ::: I will arrive at a certain point in time: at 11 am., or when you arrive. I will meet you at a certain place: in the office, or where we met last time. We can talk about sweets and mean eating sweets. ::: Literature review suggests that the relations exemplified by these pairs of sentences are different, because they connect different types of syntactic units. The first relation in each pair connects a verb and one of its arguments, the second—two clauses. ::: Such distinctions are artificial. Semantic relations link concepts, and will surface on the syntactic level on which the concepts they connect surface. ::: We aim to give an account of semantic relations that does not depend on syntactic levels. We will justify a unified view of semantic relations across syntactic levels. Such a view has a positive effect on text analysis. It will allow us to gather evidence for a particular semantic relation from all levels at which it appears. Having such information that is not separated according to syntactic levels will allow a text analysis and knowledge acquisition system to use at each processing step, all the evidence previously gathered. We will show that this translates into faster learning and better results. ::: We can take semantic relation analysis onto another level. We can look for descriptions of concepts connected by a specific semantic relation to find what characteristics or features of the concepts connected make them interact in this way. (1) blue book, happy person, interesting study; (2) paper bag, wooden chair, iron gate; (3) oak tree, cumulus cloud, flounder fish. ::: Blue, happy, interesting are properties, and paper, wood, iron are materials. Oak is a specific type of tree, cumulus is a type of cloud, and flounder is a type of fish. ::: We will use ontologies to find similarities between concepts that explain or give us indications about the semantic relations in which they are involved. ::: All these aspects we explore serve to improve text analysis. We propose a uniform processing of texts that allows us to extracts pairs of concepts that interact, and to describe this interaction through semantic relations.
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paper_title: COATIS, an NLP System to Locate Expressions of Actions Connected by Causality Links

paper_content:

COATIS is an automatic tool designed to locate certain actions expressed in texts. Rules of contextual exploration, activated by the presence of linguistic indicators of causality in sentences, enable COATIS to locate expressions that denote field actions and that are linked by causal relations. COATIS processes technical texts of any domain, in the French language. It is therefore particularly suitable for use in causal knowledge acquisition from texts.
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paper_title: Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

paper_content:

Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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paper_title: Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing

paper_content:

This study investigated how effectively cause-effect information can be extracted from newspaper text using a simple computational method (i.e. without knowledge-based inferencing and without full parsing of sentences). An automatic method was developed for identifying and extracting cause-effect information in Wall Street Journal text using linguistic clues and pattern matching. The set of linguistic patterns used for identifying causal relationships was based on a thorough review of the literature and on an analysis of sample sentences from the Wall Street Journal. The cause-effect information extracted using the method was compared with that identified by two human judges. The program successfully extracted ∼68% of the causal relationships identified by both judges (the intersection of the two sets of causal relationships identified by the judges). Of the instances that the computer program identified as causal relationships, ∼25% were identified by both judges, and 64% were identified by at least one of the judges. Problems encountered are discussed
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paper_title: Extracting Causal Knowledge From A Medical Database Using Graphical Patterns

paper_content:

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.
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paper_title: Automatic Detection of Arabic Causal Relations

paper_content:

The work described in this paper is about the automatic detection and extraction of causal relations that are explicitly expressed in Modern Standard Arabic (MSA) texts. In this initial study, a set of linguistic patterns was derived to indicate the presence of cause-effect information in sentences from open domain texts. The patterns were constructed based on a set of syntactic features which was acquired by analyzing a large untagged Arabic corpus so that parts of the sentence representing the cause and those representing the effect can be distinguished. To the best of researchers knowledge, no previous studies have dealt this type of relation for the Arabic language.
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paper_title: Another Look at Causality: Discovering Scenario-Specific Contingency Relationships with No Supervision

paper_content:

Contingency discourse relations play an important role in natural language understanding. In this paper we propose an unsupervised learning model to automatically identify contingency relationships between scenario-specific events in web news articles (on the Iraq war and on hurricane Katrina). The model generates ranked contingency relationships by identifying appropriate candidate event pairs for each scenario of a particular domain. Scenario-specific events, contributing towards the same objectives in a domain, are likely to be dependent on each other, and thus form good candidates for contingency relationships. In order to evaluate the ranked contingency relationships, we rely on the manipulation theory of causation and a comparison of precision-recall performance curves. We also perform various tests which bring insights into how people perceive causality. For example, our findings show that the larger the distance between two events, the more likely it becomes for the annotators to identify them as non-causal.
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paper_title: Extracting a Causal Network of News Topics

paper_content:

Because of the abundance of online news, it is impossible for users to process all the available information. Tools are needed to help process this information. To mitigate this challenge we propose generating a network of causally related news topics to help the user understand and navigate throughout the news. We assume that by providing the causes or effects of a news topics, the user will be able to relate current news to past news topics that the user knows about, or that the user will discover past news topics as currently relevant. Also, the additional context will facilitate the understanding of the current news topic.
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paper_title: Automatic Detection Of Causal Relations For Question Answering

paper_content:

Causation relations are a pervasive feature of human language. Despite this, the automatic acquisition of causal information in text has proved to be a difficult task in NLP. This paper provides a method for the automatic detection and extraction of causal relations. We also present an inductive learning approach to the automatic discovery of lexical and semantic constraints necessary in the disambiguation of causal relations that are then used in question answering. We devised a classification of causal questions and tested the procedure on a QA system.
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paper_title: Adverbs of Causation

paper_content:

Some logicians, and some linguists, too, have appeared to disparage word-semantics, or at least have described it as lexicography rather than semantics.1 It should be obvious, however, that even if one’s aim is the construction of a general framework for semantic theory, such a framework can only be justified if it can be applied to actual sentences of natural language; and that means to actual words of natural language. In some cases it is unnecessary to consider more than a few words in a given class. for instance, if we have an adequate semantics for Arebella we should not expect to have to agonize over Bramwell, Catherine, Dean, Evangeline, Fred, Guinevere, Howard, Isolde, Jeremy, Kiri, Ludwig, Miriam, Nathaniel, Olga, Percival, Quilla, Ralf, Stephanie, Trevor, Ursula, Vernon, Wilhelmina, Xerxes, Yvonne, or Zane. But one class of words where it does seem necessary to consider many examples is the class of adverbs. Maybe this shows that adverbs do not form a genuine semantic class, but if so, then that gives even more reason for looking at many examples. Semantically, and also syntactically, as far as I can judge, adverbs seem the least understood large class of words in natural language. There have been few studies of their semantics at all, and those that there have been are at least superficially pretty divergent.2
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paper_title: Ideas About Causation in Philosophy and Psychology

paper_content:

Philosophical theories summarized here include regularity and necessity theories from Hume to the present; manipulability theory; the theory of powerful particulars; causation as connected changes within a defined state of affairs; departures from "normal" events or from some standard for comparison; causation as a transfer of something between objects; and causal propagation and production. Issues found in this literature and of relevance for psychology include whether actual causal relations can be perceived or known; what sorts of things people believe can be causes; different levels of causal analysis; the distinction between the causal relation itself and cues to causal relations; causal frames or fields; internal and external causes; and understanding of causation in different realms of the world, such as the natural and artificial realms. A full theory of causal inference by laypeople should address all of these issues. The main purpose of this article is to survey philosophical theories of causation in a manner intended to be suitable for psychologists interested in causation. The article has two sections: The first presents brief summaries of philosophical theories of causation from Aristotle to the present. In the second, issues found in the philosophical literature are used to suggest new approaches to the study of causation in psychology.
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paper_title: Scalable Techniques for Mining Causal Structures

paper_content:

Mining for association rules in market basket data has proved a fruitful area of research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form “the existence of item A implies the existence of item B.” However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables. ::: ::: In this paper, we consider the problem of determining casual relationships, instead of mere associations, when mining market basket data. We identify some problems with the direct application of Bayesian learning ideas to mining large databases, concerning both the scalability of algorithms and the appropriateness of the statistical techniques, and introduce some initial ideas for dealing with these problems. We present experimental results from applying our algorithms on several large, real-world data sets. The results indicate that the approach proposed here is both computationally feasible and successful in identifying interesting causal structures. An interesting outcome is that it is perhaps easier to infer the lack of causality than to infer causality, information that is useful in preventing erroneous decision making.
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paper_title: Toward a natural language-based causal model acquisition system

paper_content:

Future expert systems for understanding physical mechanisms will probably employ causal models as the foundation of their expertise, and the problem of acquiring these causal models is important. This paper explores one possibility, that of acquiring causal models by understanding natural language explanations of these mechanisms. It identifies six different research issues in which understanding an explanation requires knowledge-based reasoning, and proposes approaches to these problems within an integrated natural language-based causal model acquisition system.
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paper_title: COATIS, an NLP System to Locate Expressions of Actions Connected by Causality Links

paper_content:

COATIS is an automatic tool designed to locate certain actions expressed in texts. Rules of contextual exploration, activated by the presence of linguistic indicators of causality in sentences, enable COATIS to locate expressions that denote field actions and that are linked by causal relations. COATIS processes technical texts of any domain, in the French language. It is therefore particularly suitable for use in causal knowledge acquisition from texts.
---
paper_title: Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing

paper_content:

This study investigated how effectively cause-effect information can be extracted from newspaper text using a simple computational method (i.e. without knowledge-based inferencing and without full parsing of sentences). An automatic method was developed for identifying and extracting cause-effect information in Wall Street Journal text using linguistic clues and pattern matching. The set of linguistic patterns used for identifying causal relationships was based on a thorough review of the literature and on an analysis of sample sentences from the Wall Street Journal. The cause-effect information extracted using the method was compared with that identified by two human judges. The program successfully extracted ∼68% of the causal relationships identified by both judges (the intersection of the two sets of causal relationships identified by the judges). Of the instances that the computer program identified as causal relationships, ∼25% were identified by both judges, and 64% were identified by at least one of the judges. Problems encountered are discussed
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paper_title: Extracting Causal Knowledge From A Medical Database Using Graphical Patterns

paper_content:

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.
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paper_title: Knowledge-based acquisition of causal relationships in text

paper_content:

Abstract In this paper, we describe a procedure that integrates several techniques for recognizing causal relationships in expository text. Applying these techniques yields a knowledge representation consisting of classifications of the causal relationships contained in a text. This procedure is very robust. If any one of the techniques for recognizing a causal relationship fails, an alternate methodology can be used to continue the causal analysis. The procedure we will describe is embodied in a program called the causal analyser. We have applied the causal analyser to several texts to produce a representation of the causal relationships in these texts. The causal analyser described in this paper will be part of a knowledge acquisition system called TAKT (tool for the acquisition of knowledge from text), which is currently being developed.
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paper_title: AUTOMATIC IDENTIFICATION OF CAUSAL RELATIONS IN TEXT AND THEIR USE FOR IMPROVING PRECISION IN INFORMATION RETRIEVAL

paper_content:

This study represents one attempt to make use of relations expressed in text to improve information retrieval effectiveness. In particular, the study investigated whether the information obtained by matching causal relations expressed in documents with the causal relations expressed in users' queries could be used to improve document retrieval results in comparison to using just term matching without considering relations. ::: An automatic method for identifying and extracting cause-effect information in Wall Street Journal text was developed. The method uses linguistic clues to identify causal relations without recourse to knowledge-based inferencing. The method was successful in identifying and extracting about 68% of the causal relations that were clearly expressed within a sentence or between adjacent sentences in Wall Street Journal text. Of the instances that the computer program identified as causal relations, 72% can be considered to be correct. ::: The automatic method was used in an experimental information retrieval system to identify causal relations in a database of full-text Wall Street Journal documents. Causal relation matching was found to yield a small but significant improvement in retrieval results when the weights used for combining the scores from different types of matching were customized for each query--as in an SDI or routing queries situation. The best results were obtained when causal relation matching was combined with word proximity matching (matching pairs of causally related words in the query with pairs of words that co-occur within document sentences). An analysis using manually identified causal relations indicate that bigger retrieval improvements can be expected with more accurate identification of causal relations. The best kind of causal relation matching was found to be one in which one member of the causal relation (either the cause or the effect) was represented as a wildcard that could match with any term. ::: The study also investigated whether using Roget's International Thesaurus (3rd ed.) to expand query terms with synonymous and related terms would improve retrieval effectiveness. Using Roget category codes in addition to keywords did give better retrieval results. However, the Roget codes were better at identifying the nonrelevant documents than the relevant ones.
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paper_title: Automatic Detection Of Causal Relations For Question Answering

paper_content:

Causation relations are a pervasive feature of human language. Despite this, the automatic acquisition of causal information in text has proved to be a difficult task in NLP. This paper provides a method for the automatic detection and extraction of causal relations. We also present an inductive learning approach to the automatic discovery of lexical and semantic constraints necessary in the disambiguation of causal relations that are then used in question answering. We devised a classification of causal questions and tested the procedure on a QA system.
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paper_title: Hypothesizing and Refining Causal Models

paper_content:

Abstract : An important common sense competence is the ability to hypothesize causal relations. This paper presents a set of constraints which make the problem of formulating causal hypotheses about simple physical systems a tractable one. The constraints include: 1) a temporal and physical proximity requirement; 2) a set of abstract causal explanations for changes in physical systems in terms of dependences between quantities; and 3) a teleological assumption that dependences in designed physical systems are functions. These constraints were embedded in a learning system which was tested in two domains: a sink and a toaster. The learning system successfully generated and refined naive causal models of these simple physical systems. The causal models which emerge from the learning process support causal reasoning - explanation, prediction, and planning. Inaccurate predictions and failed plans in turn indicate deficiencies in the causal models and the need to rehypothesize. Thus learning supports reasoning which leads to further learning. The learning system makes use of standard inductive rules of inference as well as the constraints of casual hypotheses to generalize its casual models. Finally, a simple example involving an analogy illustrates another way to repair incomplete causal models.
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paper_title: Causal Network Construction to Support Understanding of News

paper_content:

To support understanding of news, we propose a novel TEC model (Topic-Event Causal relation model) and describe the method to construct a Causal Network in the TEC model. The model includes two types of keywords to represent casual relations: topic keywords, which describe topics, and event keywords, which describe events. In the TEC model, causal relations are represented by an edge-labeled directed graph. A source vertex represents the cause of an event, and a destination vertex represents the result of that event. Each vertex contains event keywords and topic keywords and an importance score for each keyword. The edge label is the importance score of that causal relation. To construct a causal network, we extract causal relations from articles based on `clue phrases' , merge similar event vertices and reduce the size of the causal network based on the importance score of each causal relation. Preliminary experiments to assess the validity of the proposed method demonstrated its usefulness.
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paper_title: Automated Discovery of WordNet Relations

paper_content:

The WordNet lexical database is now quite large and offers broad coverage of general lexical relations in English. As is evident in this volume, WordNet has been employed as a resource for many applications in natural language processing (NLP) and information retrieval (IR). However, many potentially useful lexical relations are currently missing from WordNet. Some of these relations, while useful for NLP and IR applications, are not necessarily appropriate for a general, domain-independent lexical database. For example, WordNet’s coverage of proper nouns is rather sparse, but proper nouns are often very important in application tasks. The standard way lexicographers find new relations is to look through huge lists of concordance lines. However, culling through long lists of concordance lines can be a rather daunting task (Church and Hanks, 1990), so a method that picks out those lines that are very likely to hold relations of interest should be an improvement over more traditional techniques. This chapter describes a method for the automatic discovery of WordNetstyle lexico-semantic relations by searching for corresponding lexico-syntactic patterns in large text collections. Large text corpora are now widely available, and can be viewed as vast resources from which to mine lexical, syntactic, and semantic information. This idea is reminiscent of what is known as “data mining” in the artificial intelligence literature (Fayyad and Uthurusamy, 1996), however, in this case the ore is raw text rather than tables of numerical data. The Lexico-Syntactic Pattern Extraction (LSPE) method is meant to be useful as an automated or semi-automated aid for lexicographers and builders of domain-dependent knowledge-bases. The LSPE technique is light-weight; it does not require a knowledge base or complex interpretation modules in order to suggest new WordNet relations.
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paper_title: Text mining for causal relations

paper_content:

Given a semantic relation, the automatic extraction of linguistic patterns that express that relation is a rather difficult problem. This paper presents a semi-automatic method of discovering generally applicable lexico-syntactic patterns that refer to the causal relation. The patterns are found automatically, but their validation is done semi-automatically.
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paper_title: Building a Corpus of Temporal-Causal Structure

paper_content:

While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typically considered either temporal relations or causal relations, but not both, and no corpora currently exist that allow the relation between temporals and causals to be examined empirically. We have annotated a corpus of 1000 event pairs for both temporal and causal relations, focusing on a relatively frequent construction in which the events are conjoined by the word and. Temporal relations were annotated using an extension of the BEFORE and AFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on connective phrases like and as a result. The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on causal relations. Analysis of the resulting corpus revealed some interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORE relation. The corpus was also explored using machine learning methods, and while model performance exceeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult temporal and causal relations.
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paper_title: Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

paper_content:

We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture! exacerbate desertification from the web using semantic relation (between nouns), context, and association features. Experiments show that our method outperforms baselines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture! exacerbate desertification! increase Asian dust (from China)! asthma gets worse. Experiments show that we can generate 50,000 scenarios with 68% precision. We also generated a scenario deforestation continues! global warming worsens! sea temperatures rise! vibrio parahaemolyticus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we “predicted” the future event sequence in a sense.
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paper_title: Ontology enrichment with causation relations

paper_content:

Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. However it suffers from a lack of standards to define concepts, besides the lack of fully automatic knowledge acquisition methods. In performing this learning process, the discovery of non-taxonomic relationships has been identified as being the most difficult. This study is then an attempt to create an enhanced framework for discovering and classifying ontological relationships by using a machine learning strategy. We take into consideration the context of the input text in performing the classification of the semantic relations, in particular, causation relations. The proposed framework extracts initial semantic patterns for causation relation from the input samples, then filters these patterns using two novel algorithms, namely, the “Purpose Based Word Sense Disambiguation” which helps in determining the causation senses for input pair of words and the “Graph Based Semantics” which determines the existence of the causation relations in the sentence and to extract their cause-effect parts. The results show a good performance and the implemented framework cut off many steps of the usual process to produce the final results.
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paper_title: Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing

paper_content:

This study investigated how effectively cause-effect information can be extracted from newspaper text using a simple computational method (i.e. without knowledge-based inferencing and without full parsing of sentences). An automatic method was developed for identifying and extracting cause-effect information in Wall Street Journal text using linguistic clues and pattern matching. The set of linguistic patterns used for identifying causal relationships was based on a thorough review of the literature and on an analysis of sample sentences from the Wall Street Journal. The cause-effect information extracted using the method was compared with that identified by two human judges. The program successfully extracted ∼68% of the causal relationships identified by both judges (the intersection of the two sets of causal relationships identified by the judges). Of the instances that the computer program identified as causal relationships, ∼25% were identified by both judges, and 64% were identified by at least one of the judges. Problems encountered are discussed
---
paper_title: Extracting Causal Knowledge From A Medical Database Using Graphical Patterns

paper_content:

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.
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paper_title: Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts

paper_content:

Various supervised algorithms for mining causal relations from large corpora exist. These algorithms have focused on relations explicitly expressed with causal verbs, e.g. "to cause". However, the challenges of extracting causal relations from domain-specific texts have been overlooked. Domain-specific texts are rife with causal relations that are implicitly expressed using verbal and non-verbal patterns, e.g. "reduce", "drop in", "due to". Also, readily-available resources to support supervised algorithms are inexistent in most domains. To address these challenges, we present a novel approach for causal relation extraction. Our approach is minimally-supervised, alleviating the need for annotated data. Also, it identifies both explicit and implicit causal relations. Evaluation results revealed that our technique achieves state-of-the-art performance in extracting causal relations from domain-specific, sparse texts. The results also indicate that many of the domain-specific relations were unclassifiable in existing taxonomies of causality.
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paper_title: Incremental Construction of Causal Network from News Articles

paper_content:

We propose a novel method for the incremental construction of causal networks to clarify the relationships among news events. We propose the Topic-Event Causal (TEC) model as a causal network model and an incremental constructing method based on it. In the TEC model, a causal relation is expressed using a directed graph and a vertex representing an event. A vertex contains structured keywords consisting of topic keywords and an SVO tuple. An SVO tuple, which consists of a tuple of subject, verb and object keywords represent the details of the event. To obtain a chain of causal relations, vertices representing a similar event need to be detected. We reduce the time taken to detect them by restricting the calculation to topics using topic keywords. We detect them on a concept level. We propose an identification method that identifies the sense of the keywords and introduce three semantic distance methods to compare keywords. Our method detects vertices representing similar events more precisely than conventional methods. We carried out experiments to validate the proposed methods.
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paper_title: Automatic Detection of Arabic Causal Relations

paper_content:

The work described in this paper is about the automatic detection and extraction of causal relations that are explicitly expressed in Modern Standard Arabic (MSA) texts. In this initial study, a set of linguistic patterns was derived to indicate the presence of cause-effect information in sentences from open domain texts. The patterns were constructed based on a set of syntactic features which was acquired by analyzing a large untagged Arabic corpus so that parts of the sentence representing the cause and those representing the effect can be distinguished. To the best of researchers knowledge, no previous studies have dealt this type of relation for the Arabic language.
---
paper_title: Another Look at Causality: Discovering Scenario-Specific Contingency Relationships with No Supervision

paper_content:

Contingency discourse relations play an important role in natural language understanding. In this paper we propose an unsupervised learning model to automatically identify contingency relationships between scenario-specific events in web news articles (on the Iraq war and on hurricane Katrina). The model generates ranked contingency relationships by identifying appropriate candidate event pairs for each scenario of a particular domain. Scenario-specific events, contributing towards the same objectives in a domain, are likely to be dependent on each other, and thus form good candidates for contingency relationships. In order to evaluate the ranked contingency relationships, we rely on the manipulation theory of causation and a comparison of precision-recall performance curves. We also perform various tests which bring insights into how people perceive causality. For example, our findings show that the larger the distance between two events, the more likely it becomes for the annotators to identify them as non-causal.
---
paper_title: Extracting a Causal Network of News Topics

paper_content:

Because of the abundance of online news, it is impossible for users to process all the available information. Tools are needed to help process this information. To mitigate this challenge we propose generating a network of causally related news topics to help the user understand and navigate throughout the news. We assume that by providing the causes or effects of a news topics, the user will be able to relate current news to past news topics that the user knows about, or that the user will discover past news topics as currently relevant. Also, the additional context will facilitate the understanding of the current news topic.
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paper_title: Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis

paper_content:

Human beings generally analyze information with some kinds of semantic expectations. This not only speeds up the processing time, it also helps to put the analysis in the correct context and perspective. To capitalize on this type of intelligent human behavior, this paper proposes a semantic expectation-based knowledge extraction methodology (SEKE) for extracting causation relations from text. In particular, we study the application of a causation semantic template on the Hong Kong Stock market movement (Hang Seng Index) with English financial news from Reuters, South China Morning Post and Hong Kong Standard. With one-month data input and over a two-month testing period, the system shows that it can correctly analyzes single reason sentences with about 76% precision and 74% recall rates. If partial reason extraction (two out of one reason) is included and weighted by a factor of 0.5, the performance is improved to about 83% and 81% respectively. As the proposed framework is language independent, we expect cross lingual knowledge extraction can work better with this semantic expectation-based framework.
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paper_title: Extracting Causation Knowledge from Natural Language Texts

paper_content:

SEKE2 is a semantic expectation-based knowledge extraction system for extracting causation relations from natural language texts. It is inspired by capitalizing the human behavior of analyzing information with semantic expectations. The framework of SEKE2 consists of different kinds of generic templates organized in a hierarchical fashion. All kinds of templates are domain independent. They are robust and enable flexible changes for different domains and expected semantics. By associating a causation semantic template with a set of sentence templates, SEKE2 can extract causation knowledge from complex sentences without full-fledged syntactic parsing. To demonstrate the flexibility of SEKE2 for different domains, we study the application of causation semantic templates on two domain areas of news stories, namely, Hong Kong stock market movement and global warming.
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paper_title: Causal Relation Extraction

paper_content:

This paper presents a supervised method for the detection and extraction of Causal Relations from open domain text. First we give a brief outline of the definition of causation and how it relates to other Semantic Relations, as well as a characterization of their encoding. In this work, we only consider marked and explicit causations. Our approach first identifies the syntactic patterns that may encode a causation, then we use Machine Learning techniques to decide whether or not a pattern instance encodes a causation. We focus on the most productive pattern, a verb phrase followed by a relator and a clause, and its reverse version, a relator followed by a clause and a verb phrase. As relators we consider the words as, after, because and since. We present a set of lexical, syntactic and semantic features for the classification task, their rationale and some examples. The results obtained are discussed and the errors analyzed.
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paper_title: Models of causation and the semantics of causal verbs

paper_content:

Abstract This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation ( Cheng, 1997 ; Cheng and Novick (1991 , Cheng and Novick (1992 ), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on Talmy’s (1988) theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.
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paper_title: Open-Domain Semantic Role Labeling by Modeling Word Spans

paper_content:

Most supervised language processing systems show a significant drop-off in performance when they are tested on text that comes from a domain significantly different from the domain of the training data. Semantic role labeling techniques are typically trained on newswire text, and in tests their performance on fiction is as much as 19% worse than their performance on newswire text. We investigate techniques for building open-domain semantic role labeling systems that approach the ideal of a train-once, use-anywhere system. We leverage recently-developed techniques for learning representations of text using latent-variable language models, and extend these techniques to ones that provide the kinds of features that are useful for semantic role labeling. In experiments, our novel system reduces error by 16% relative to the previous state of the art on out-of-domain text.
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paper_title: Text mining for causal relations

paper_content:

Given a semantic relation, the automatic extraction of linguistic patterns that express that relation is a rather difficult problem. This paper presents a semi-automatic method of discovering generally applicable lexico-syntactic patterns that refer to the causal relation. The patterns are found automatically, but their validation is done semi-automatically.
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paper_title: UTD: Classifying Semantic Relations by Combining Lexical and Semantic Resources

paper_content:

This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation type-specific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-existing relations of the nominals. This approach achieved an F1 score of 82.19% and an accuracy of 77.92%.
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paper_title: Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

paper_content:

Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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paper_title: SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals

paper_content:

SemEval-2 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals. The task was designed to compare different approaches to semantic relation classification and to provide a standard testbed for future research. This paper defines the task, describes the training and test data and the process of their creation, lists the participating systems (10 teams, 28 runs), and discusses their results.
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paper_title: Extracting Causal Knowledge From A Medical Database Using Graphical Patterns

paper_content:

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.
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paper_title: Classification of semantic relations between nominals

paper_content:

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of semantic relations in text. We present the development and evaluation of a semantic analysis task: automatic recognition of relations between pairs of nominals in a sentence. The task was part of SemEval-2007, the fourth edition of the semantic evaluation event previously known as SensEval. Apart from the observations we have made, the long-lasting effect of this task may be a framework for comparing approaches to the task. We introduce the problem of recognizing relations between nominals, and in particular the process of drafting and refining the definitions of the semantic relations. We show how we created the training and test data, list and briefly describe the 15 participating systems, discuss the results, and conclude with the lessons learned in the course of this exercise.
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paper_title: Extracting Action and Event Semantics from Web Text

paper_content:

Most information extraction research identifies the state of the world in text, including the entities and the relationships that exist between them. Much less attention has been paid to the understanding of dynamics, or how the state of the world changes over time. Because intelligent behavior seeks to change the state of the world in rational and utilitymaximizing ways, common-sense knowledge about dynamics is essential for intelligent agents. In this paper, we describe a novel system, PREPOST, that tackles the problem of extracting the preconditions and effects of actions and events, two important kinds of knowledge for connecting world state and the actions that affect it. In experiments on Web text, PREPOST is able to improve by 79% over a baseline technique for identifying the effects of actions (64% improvement for preconditions).
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paper_title: Commonsense Causal Reasoning Using Millions of Personal Stories

paper_content:

The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.
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paper_title: A Non-Projective Dependency Parser

paper_content:

We describe a practical parser for unrestricted dependencies. The parser creates links between words and names the links according to their syntactic functions. We first describe the older Constraint Grammar parser where many of the ideas come from. Then we proceed to describe the central ideas of our new parser. Finally, the parser is evaluated.
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paper_title: A Maximum-Entropy-Inspired Parser

paper_content:

We present a new parser for parsing down to Penn tree-bank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less, and 89.5% for sentences of length 100 and less when trained and tested on the previously established [5, 9, 10, 15, 17] "standard" sections of the Wall Street Journal treebank. This represents a 13% decrease in error rate over the best single-parser results on this corpus [9]. The major technical innovation is the use of a "maximum-entropy-inspired" model for conditioning and smoothing that let us successfully to test and combine many different conditioning events. We also present some partial results showing the effects of different conditioning information, including a surprising 2% improvement due to guessing the lexical head's pre-terminal before guessing the lexical head.
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paper_title: Causal relation extraction using cue phrase and lexical pair probabilities

paper_content:

This work aims to extract causal relations that exist between two events expressed by noun phrases or sentences. The previous works for the causality made use of causal patterns such as causal verbs. We concentrate on the information obtained from other causal event pairs. If two event pairs share some lexical pairs and one of them is revealed to be causally related, the causal probability of another event pair tends to increase. We introduce the lexical pair probability and the cue phrase probability. These probabilities are learned from raw corpus in unsupervised manner. With these probabilities and the Naive Bayes classifier, we try to resolve the causal relation extraction problem. Our inter-NP causal relation extraction shows the precision of 81.29%, that is 7.05% improvement over the baseline model. The proposed models are also applied to inter-sentence causal relation extraction.
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paper_title: Automatic Detection Of Causal Relations For Question Answering

paper_content:

Causation relations are a pervasive feature of human language. Despite this, the automatic acquisition of causal information in text has proved to be a difficult task in NLP. This paper provides a method for the automatic detection and extraction of causal relations. We also present an inductive learning approach to the automatic discovery of lexical and semantic constraints necessary in the disambiguation of causal relations that are then used in question answering. We devised a classification of causal questions and tested the procedure on a QA system.
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paper_title: An Unsupervised Approach To Recognizing Discourse Relations

paper_content:

We present an unsupervised approach to recognizing discourse relations of CONTRAST, EXPLANATION-EVIDENCE, CONDITION and ELABORATION that hold between arbitrary spans of texts. We show that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases.
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paper_title: JU: A Supervised Approach to Identify Semantic Relations from Paired Nominals

paper_content:

This article presents the experiments carried out at Jadavpur University as part of the participation in Multi-Way Classification of Semantic Relations between Pairs of Nomi-nals in the SemEval 2010 exercise. Separate rules for each type of the relations are identified in the baseline model based on the verbs and prepositions present in the segment between each pair of nominals. Inclusion of WordNet features associated with the paired nominals play an important role in distinguishing the relations from each other. The Conditional Random Field (CRF) based machine-learning framework is adopted for classifying the pair of nominals. Application of dependency relations, Named Entities (NE) and various types of WordNet features along with several combinations of these features help to improve the performance of the system. Error analysis suggests that the performance can be improved by applying suitable strategies to differentiate each paired nominal in an already identified relation. Evaluation result gives an overall macro-averaged F1 score of 52.16%.
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paper_title: An open-domain cause-effect relation detection from paired nominals

paper_content:

We present a supervised method for detecting causal relations from text. Various kinds of dependency relations, WordNet features, Parts-of-Speech (POS) features along with several combinations of these features help to improve the performance of our system. In our experiments, we used SemEval-2010 Task #8 data sets. This system used 7954 instances for training and 2707 instances for testing from Task #8 datasets. The J48 algorithm was used to identify semantic causal relations in a pair of nominals. Evaluation result gives an overall F1 score of 85.8% of causal instances.
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paper_title: Learning Textual Graph Patterns to Detect Causal Event Relations

paper_content:

This paper presents a novel method for discovering causal relations between events encoded in text. In order to determine if two events from the same sentence are in a causal relation or not, we first build a graph representation of the sentence that encodes lexical, syntactic, and semantic information. In a second step, we automatically extract multiple graph patterns (or subgraphs) from such graph representations and sort them according to their relevance in determining the causality between two events from the same sentence. Finally, in order to decide if these events are causal or not, we train a binary classifier based on what graph patterns can be mapped to the graph representation associated with the two events. Our experimental results show that capturing the feature dependencies of causal event relations using a graph representation significantly outperforms an existing method that uses a flat representation of features.
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paper_title: Multi level causal relation identification using extended features

paper_content:

Extracting causal relation underlying natural language is an important issue in knowledge discovery. Most previous studies of casual relation extraction focus on simple cases like causal relations between two noun phrases indicated by fixed verbs or prepositions. For more complicated causal relations, such as causal relations between clauses, the previously developed algorithm may not work. To solve this problem, this paper develops a system that is able to extract causal relations in multi-level language expressions such as, words, phrases and clauses without fixed relators. The information extraction system is composed of a multi-level relation extractor and an ensemble-based relation classifier. It may extract more subtypes of causal relations than previous work because extracting domain is expanded in terms of both syntactic expressions and semantic meanings. In addition, the proposed method outperforms previously developed methods because extended features based on lexical semantic resources are explored. Experiments show that our system achieves an accuracy of 88.69% and F-score of 0.6637 in a dataset with 300 sentences.
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paper_title: Learning Causality for News Events Prediction

paper_content:

The problem we tackle in this work is, given a present news event, to generate a plausible future event that can be caused by the given event. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precise labeled causality examples, we mine 150 years of news articles, and apply semantic natural language modeling techniques to titles containing certain predefined causality patterns. For generalization, the model uses a vast amount of world knowledge ontologies mined from LinkedData, containing ~200 datasets with approximately 20 billion relations. Empirical evaluation on real news articles shows that our Pundit algorithm reaches a human-level performance.
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paper_title: Automatic Extraction of Cause-Effect Relations in Natural Language Text

paper_content:

The discovery of causal relations from text has been studied adopting various approaches based on rules or Machine Learning (ML) techniques. The approach proposed joins both rules and ML methods to combine the advantage of each one. In particular, our approach first identifies a set of plausible cause-effect pairs through a set of logical rules based on dependencies between words then it uses Bayesian inference to reduce the number of pairs produced by ambiguous patterns. The SemEval-2010 task 8 dataset challenge has been used to evaluate our model. The results demonstrate the ability of the rules for the relation extraction and the improvements made by the filtering process.
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paper_title: gSpan: graph-based substructure pattern mining

paper_content:

We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.
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paper_title: Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations

paper_content:

Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. We trained machine learning models using features derived from WordNet and the Google N-gram corpus, and they outperformed a variety of baselines, achieving an F-measure of 49.0 for temporals and 52.4 for causals. Analysis of these models suggests that additional data will improve performance, and that temporal information is crucial to causal relation identification.
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paper_title: Generating Typed Dependency Parses from Phrase Structure Parses

paper_content:

This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar and the Link parser. The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.
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paper_title: Causal Association Mining from Geriatric Literature

paper_content:

Literature pertaining to geriatric care contains rich information regarding the best practices related to geriatric health care issues. The publication domain of geriatric care is small as compared to other health related areas, however, there are over a million articles pertaining to different cases and case interventions capturing best practice outcomes. The knowledge extracted from these articles could be harvested and translated from research to practice in a quicker and more efficient manner. Geriatric literature contains multiple domains that contain information such as interventions, information on care for elderly, case studies and real life scenarios. These articles contain a variety of causal relationships such as the relationship between interventions and disorders. The goal of this study is to identify these causal relations from published abstracts. Natural language processing and statistical methods were adopted to identify and extract these causal relations with a precision of 79.54% and recall of 81%.
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paper_title: Verbnet: a broad-coverage, comprehensive verb lexicon

paper_content:

Despite the proliferation of approaches to lexicon development, the field of natural language processing has yet to develop a clear consensus on guidelines for computational verb lexicons, which has severely limited their utility in information processing applications. James Pustejovsky's Generative Lexicon has concentrated on nouns rather than verbs. WordNet does not provide a comprehensive account of possible syntactic frames and predicate argument structures associated with individual verb senses and ComLex provides syntactic frames but ignores sense distinctions. Dorr's LCS lexicon attempts to address these limitations, but does not provide broad coverage of syntactic frames or different senses or links to actual instances in corpora. ::: In order to address this gap, we created VerbNet, a verb lexicon compatible with Word-Net but with explicitly stated syntactic and semantic information, using Levin verb classes to systematically construct lexical entries. Classes are hierarchically organized to ensure that all their members have common semantic and syntactic properties. Each class in the hierarchy is characterized extensionally by its set of verbs, and intensionally by syntactic frames and semantic predicates and a list of typical verb arguments. ::: One of VerbNet's primary applications has been as a basis for Parameterized Action Representations (PARs), which are used to animate the actions of virtual human agents in a simulated 3D environment. In order to support the animation of the actions, PARs have to make explicit many details that are often underspecified in the language. This detailed level of representation also provides a suitable pivot representation for generation in other natural languages, i.e., a form of interlingua. ::: To evaluate VerbNet's syntactic coverage it has been mapped to the Proposition Bank. VerbNet syntactic frames account for over 84% exact matches to the frames found in PropBank. ::: VerbNet provides mappings between its verbs and WordNet senses and between its verbs and FameNet II frames, and mappings between the syntactic frames and Xtag tree families. All these resources are complementary and can be used as extensions of each other. ::: The original set of classes described by Levin has been refined and extended in many ways through systematic efforts: the coverage experiment against PropBank corpus instances proposed a large set of new syntactic frames and a better treatment of prepositions; new classes from Korhonen and Briscoe's resource were integrated into the lexicon; and new members from the LCS database were added. ::: Taking advantage of VerbNet's class-based approach automatic acquisition methods were investigated. Additional verbs derived from Kingsbury's clustering experiments and from Loper's VerbNet-WordNet correlation experiment were integrated into the lexicon. These experiments show that it is possible to semi-automatically supplement and tune VerbNet with novel information from corpus data. These approaches reduce the manual classification and enable easy adaptation of the lexicon to specific tasks and applications.
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paper_title: The Berkeley FrameNet Project

paper_content:

FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year (NSF IRI-9618838, "Tools for Lexicon Building"). The project's key features are (a) a commitment to corpus evidence for semantic and syntactic generalizations, and (b) the representation of the valences of its target words (mostly nouns, adjectives, and verbs) in which the semantic portion makes use of frame semantics. The resulting database will contain (a) descriptions of the semantic frames underlying the meanings of the words described, and (b) the valence representation (semantic and syntactic) of several thousand words and phrases, each accompanied by (c) a representative collection of annotated corpus attestations, which jointly exemplify the observed linkings between "frame elements" and their syntactic realizations (e.g. grammatical function, phrase type, and other syntactic traits). This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.
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paper_title: Minimally Supervised Event Causality Identification

paper_content:

This paper develops a minimally supervised approach, based on focused distributional similarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identifying causality, we observe that discourse connectives and the particular discourse relation they evoke in context provide additional information towards determining causality between events. We show that combining discourse relation predictions and distributional similarity methods in a global inference procedure provides additional improvements towards determining event causality.
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paper_title: Event causality extraction based on connectives analysis

paper_content:

Causality is an important type of relation which is crucial in numerous tasks, such as predicting future events, generating scenario, question answering, textual entailment and discourse comprehension. Therefore, causality extraction is a fundamental task in text mining. Many efforts have been dedicated to extracting causality from texts utilizing patterns, constraints and machine learning techniques. This paper presents a new Restricted Hidden Naive Bayes model to extract causality from texts. Besides some commonly used features, such as contextual features, syntactic features, position features, we also utilize a new category feature of causal connectives. This new feature is obtained from the tree kernel similarity of sentences containing connectives. In previous studies, the features have been usually assumed to be independent, which is not the case in reality. The advantage of our model lies in its ability to cope with partial interactions among features so as to avoid over-fitting problem on Hidden Naive Bayes model, especially the interaction between the connective category and the syntactic structure of sentences. Evaluation on a public dataset shows that our method goes beyond all the baselines.
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paper_title: Causal relation of queries from temporal logs

paper_content:

In this paper, we study a new problem of mining causal relation of queries in search engine query logs. Causal relation between two queries means event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the causal relation of queries is mined by the geometric features of the events. Finally the Granger Causality Test (GCT) is utilized to further re-rank the causal relation of queries according to their GCT coefficients. In addition, we develop a 2-dimensional visualization tool to display the detected relationship of events in a more intuitive way. The experimental results on the MSN search engine query logs demonstrate that our approach can accurately detect the events in temporal query logs and the causal relation of queries is detected effectively.
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paper_title: Text mining for causal relations

paper_content:

Given a semantic relation, the automatic extraction of linguistic patterns that express that relation is a rather difficult problem. This paper presents a semi-automatic method of discovering generally applicable lexico-syntactic patterns that refer to the causal relation. The patterns are found automatically, but their validation is done semi-automatically.
---
paper_title: A Non-Projective Dependency Parser

paper_content:

We describe a practical parser for unrestricted dependencies. The parser creates links between words and names the links according to their syntactic functions. We first describe the older Constraint Grammar parser where many of the ideas come from. Then we proceed to describe the central ideas of our new parser. Finally, the parser is evaluated.
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Title: Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey

Section 1: INTRODUCTION
Description 1: Provide an overview of the challenges, significance, and previous research on the automatic extraction of causal relations from natural language texts.

Section 2: NON-STATISTICAL TECHNIQUES
Description 2: Discuss the early approaches to causal relation extraction that rely on linguistic, syntactic, and semantic pattern matching.

Section 3: STATISTICAL & MACHINE LEARNING TECHNIQUES
Description 3: Explain the shift towards statistical methods and machine learning techniques for extracting causal relations, highlighting key research and methods used.

Section 4: COMPARING THE TWO PARADIGMS
Description 4: Compare and contrast non-statistical techniques with machine learning approaches, analyzing their strengths, weaknesses, and areas of application.

Section 5: CONCLUSION & FUTURE WORK
Description 5: Summarize the findings of the survey and suggest potential directions for future research in the field of automatic causal relation extraction.