A survey on how to cross-reference web information sources
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paper_title: Verb Semantics And Lexical Selection

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This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
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paper_title: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

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This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
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paper_title: X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies

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Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches for computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web.
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paper_title: Noun Classification From Predicate-Argument Structures

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A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification.
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paper_title: The Google Similarity Distance

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Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers, the equivalent of "society" is "database," and the equivalent of "use" is "a way to search the database". We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we use the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the WWW using Google page counts. The WWW is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87 percent with the expert crafted WordNet categories
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paper_title: WordNet::Similarity - Measuring The Relatedness Of Concepts

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WordNet: Similarity is a freely available software package that makes it possible to measure the semantic similarity or relatedness between a pair of concepts (or word senses). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.
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paper_title: A Survey of Paraphrasing and Textual Entailment Methods

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Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.
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paper_title: Dependency-Based Construction of Semantic Space Models

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Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive science and natural language processing: semantic priming, synonymy detection, and word sense disambiguation. In all cases, our framework obtains results that are comparable or superior to the state of the art.
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paper_title: Bleu: A Method For Automatic Evaluation Of Machine Translation

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Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
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paper_title: A Survey of Paraphrasing and Textual Entailment Methods

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Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.
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paper_title: Robust Textual Inference Via Graph Matching

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We present a system for deciding whether a given sentence can be inferred from text. Each sentence is represented as a directed graph (extracted from a dependency parser) in which the nodes represent words or phrases, and the links represent syntactic and semantic relationships. We develop a learned graph matching approach to approximate entailment using the amount of the sentence's semantic content which is contained in the text. We present results on the Recognizing Textual Entailment dataset (Dagan et al., 2005), and show that our approach outperforms Bag-Of-Words and TF-IDF models. In addition, we explore common sources of errors in our approach and how to remedy them.
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paper_title: Learning To Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment

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We address the text-to-text generation problem of sentence-level paraphrasing --- a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our approach applies multiple-sequence alignment to sentences gathered from unannotated comparable corpora: it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences. The results of our evaluation experiments show that the system derives accurate paraphrases, outperforming baseline systems.
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paper_title: Logic Form Transformation Of WordNet And Its Applicability To Question Answering

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WordNet is a rich source of world knowledge from which formal axioms can be derived. In this paper we present a method for transforming the WordNet glosses into logic forms and further into axioms. The transformation of WordNet glosses into logic forms is useful for theorem proving and other applications. The paper demonstrates the utility of the WordNet axioms in a question answering system to rank and extract answers.
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paper_title: Semantic Role Labeling: An Introduction to the Special Issue

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Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.
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paper_title: Recognising Textual Entailment With Logical Inference

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We use logical inference techniques for recognising textual entailment. As the performance of theorem proving turns out to be highly dependent on not readily available background knowledge, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. Finally, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap; the resulting hybrid model achieves high accuracy on the RTE testset, given the state of the art. Our results also show that the different techniques that we employ perform very differently on some of the subsets of the RTE corpus and as a result, it is useful to use the nature of the dataset as a feature.
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paper_title: The Berkeley FrameNet Project

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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: Re-Evaluating Machine Translation Results With Paraphrase Support

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In this paper, we present ParaEval, an automatic evaluation framework that uses paraphrases to improve the quality of machine translation evaluations. Previous work has focused on fixed n-gram evaluation metrics coupled with lexical identity matching. ParaEval addresses three important issues: support for paraphrase/synonym matching, recall measurement, and correlation with human judgments. We show that ParaEval correlates significantly better than BLEU with human assessment in measurements for both fluency and adequacy.
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paper_title: Discovery of Inference Rules for Question Answering

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One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as ‘X writes Y’ implies ‘X is the author of Y’ in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.
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paper_title: A system for detecting and tracking internet news event

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News event detection is the task of discovering relevant, yet previously unreported real-life events and reporting it to users in human-readable form, while event tracking aims to automatically assign event labels to news stories when they arrive. A new method and system for performing the event detection and tracking task is proposed in this paper. The event detection and tracking method is based on subject extraction and an improved support vector machine (SVM), in which subject concepts can concisely and precisely express the meaning of a longer text. The improved SVM first prunes the negative examples, reserves and deletes a negative sample according to distance and class label, then trains the new set with SVM to obtain a classifier and maps the SVM outputs into probabilities. The experimental results with the real-world data sets indicate the proposed method is feasible and advanced.
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paper_title: Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries

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This paper presents a method for the detection of occasional or volatile local events using topic extraction technologies. This is a new application of topic extraction technologies that has not been addressed in general location-based services. A two-level hierarchical clustering method was applied to topics and their transitions using time-series blog entries collected with search queries including place names. According to experiments using 764 events from 37 locations in Tokyo and its vicinity, our method achieved 77.0% event findability. It was found that the number of blog entries in urban areas was sufficient for the extraction of topics, and the proposed method could extract typical volatile events, such as performances of music groups, and places of interest, such as popular restaurants.
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paper_title: Event Recognition on News Stories and Semi-Automatic Population of an Ontology

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This paper describes a system which recognizes events on news stories. Our system classifies stories and populates a hand-crafted ontology with new instances of classes defined in it. Currently, our system recognizes events which can be classified as belonging to a single category and it also recognizes overlapping events within one article (more than one event is recognized). In each case, the system provides a confidence value associated to the suggested classification. Our system uses Information Extraction and Machine Learning technologies. The system was tested using a corpus of 200 news articles from an archive of electronic news stories describing the academic life of the Knowledge Media (KMi). In particular, these news stories describe events such as a project award, publications, visits, etc.)
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paper_title: Event Extraction from Biomedical Papers Using a Full Parser

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We have designed and implemented an information extraction system using a full parser to investigate the plausibility of full analysis of text using general-purpose parser and grammar applied to biomedical domain. We partially solved the problems of full parsing of inefficiency, ambiguity, and low coverage by introducing the preprocessors, and proposed the use of modules that handles partial results of parsing for further improvement. Our approach makes it possible to modularize the system, so that the IEsystem as a whole becomes easy to be tuned to specific domains, and easy to be maintained and improved by incorporating various techniques of disambiguation, speed up, etc. In preliminary experiment, from 133 argument structures that should be extracted from 97 sentences, we obtained 23% uniquely and 24% with ambiguity. And 20% are extractable from not complete but partial results of full parsing.
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paper_title: High-precision biological event extraction with a concept recognizer

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We approached the problems of event detection, argument identification, and negation and speculation detection as one of concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually-written rules. We achieved state-of-the-art precision for two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. ::: ::: The OpenDMAP system and the rule set are available at bionlp.sourceforge.net.
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paper_title: Event Extraction and Visualization for Obtaining Personal Experiences from Blogs

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Internet users write blogs related to their personal experience, daily news, and so on. Though we can obtain blogs about personal experience using search engines on the Web, the search engines also output blogs about other topics unrelated to personal experiences. Therefore, we need to take too much time to read all blogs for obtaining those about personal experiences. This paper proposes a support system for obtaining blogs about personal experience efficiently. The system extracts three keywords that denote place, object , and action from a blog. The three keywords describe an event that leads a person to write a blog about personal experience. The system expresses the event with three pictures related to the extracted keywords. The pictures help users to judge whether personal experiences are written in the blog or not. We experimented with the system, and verified that it supports users to obtain personal experiences efficiently.
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paper_title: Event pattern discovery from the stock market bulletin

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Electronic information grows rapidly as the Internet is widely used in our daily life. In order to identify the exact information for the user query, information extraction is widely researched and investigated. The template, which pertains to events or situations, and contains slots that denote who did what to whom, when, and where, is predefined by a template builder. Therefore, fixed templates are the main obstacles for the information extraction system out of the laboratory. In this paper, a method to automatically discover the event pattern in Chinese from stock market bulletin is introduced. It is based on the tagged corpus and the domain model. The pattern discovery process is independent of the domain model by introducing a link table. The table is the connection between text surface structure and semantic deep structure represented by a domain model. The method can be easily adapted to other domains by changing the link table.
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paper_title: Enhanced Services for Targeted Information Retrieval by Event Extraction and Data Mining

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Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.
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paper_title: Real-Time News Event Extraction for Global Crisis Monitoring

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This paper presents a real-time news event extraction system developed by the Joint Research Centre of the European Commission. It is capable of accurately and efficiently extracting violent and disaster events from online news without using much linguistic sophistication. In particular, in our linguistically relatively lightweight approach to event extraction, clustered news have been heavily exploited at various stages of processing. The paper describes the system's architecture, news geo-tagging, automatic pattern learning, pattern specification language, information aggregation, the issues of integrating event information in a global crisis monitoring system and new experimental evaluation.
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paper_title: Unsupervised event extraction from biomedical literature using co-occurrence information and basic patterns

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In this paper, we propose a new unsupervised method of extracting events from biomedical literature, which uses the score measures of events and patterns having reciprocal effects on each other. We, first, generate candidate events by performing linguistic preprocessing and utilizing basic event pattern information, and then extract reliable events based on the event score which is estimated by using co-occurrence information of candidate event’s arguments and pattern score. Unlike the previous approaches, the proposed approach does not require a huge number of rules and manually constructed training corpora. ::: ::: Experimental results on GENIA corpora show that the proposed method can achieve high recall (69.7%) as well as high precision (90.3%).
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paper_title: Learning by googling

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The goal of giving a well-defined meaning to information is currently shared by endeavors such as the Semantic Web as well as by current trends within Knowledge Management. They all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. However, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. Certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. Furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.As a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API. In particular, we present PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.
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Title: A Survey on How to Cross-Reference Web Information Sources

Section 1: INTRODUCTION
Description 1: Write about the increasing quantity of web data, the challenges in analyzing and retrieving information, and the need for improving cross-referencing of web information sources.

Section 2: CONTEXT
Description 2: Discuss the concept of Semantic Measure and its importance in evaluating the semantic links between concepts as a foundation for cross-referencing methods.

Section 3: Semantic Measure Types
Description 3: Evaluate the different types of Semantic Measures, including semantic relatedness, semantic similarity, and semantic distance.

Section 4: Knowledge Resources Types
Description 4: Describe the various knowledge resources used for evaluating semantic measures, including structured sources (ontologies and DBPedia), semi-structured sources (Wikipedia), and unstructured sources (Web).

Section 5: CROSS-REFERENCING METHODS PROPOSALS
Description 5: Present the main cross-referencing methods for web information sources, focusing on semantic measures, paraphrase identification, and event extraction.

Section 6: Semantic Measures
Description 6: Discuss various techniques to measure semantic similarity including path length-based measures, information content-based measures, feature-based measures, and distributional-based measures, and hybrid measures.

Section 7: Paraphrase Identification
Description 7: Explore paraphrase and textual entailment recognition and extraction methods and their role in improving cross-referencing methods.

Section 8: Event Extraction
Description 8: Examine different event extraction techniques such as data-driven, knowledge-driven, and hybrid event extraction to facilitate cross-referencing methods.

Section 9: DISCUSSION
Description 9: Summarize the discussed approaches to cross-referencing web information sources, emphasize the importance of selecting suitable event extraction techniques, and highlight the need for further research.

Section 10: CONCLUSION
Description 10: Conclude by underlining the significance of cross-referencing web information sources and the expected future developments in this research field.