Obtaining Information Content of Concepts: An Overview
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paper_title: A Semantic Approach To IE Pattern Induction

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This paper presents a novel algorithm for the acquisition of Information Extraction patterns. The approach makes the assumption that useful patterns will have similar meanings to those already identified as relevant. Patterns are compared using a variation of the standard vector space model in which information from an ontology is used to capture semantic similarity. Evaluation shows this algorithm performs well when compared with a previously reported document-centric approach.
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paper_title: A semantic similarity metric combining features and intrinsic information content

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In many research fields such as Psychology, Linguistics, Cognitive Science and Artificial Intelligence, computing semantic similarity between words is an important issue. In this paper a new semantic similarity metric, that exploits some notions of the feature-based theory of similarity and translates it into the information theoretic domain, which leverages the notion of Information Content (IC), is presented. In particular, the proposed metric exploits the notion of intrinsic IC which quantifies IC values by scrutinizing how concepts are arranged in an ontological structure. In order to evaluate this metric, an on line experiment asking the community of researchers to rank a list of 65 word pairs has been conducted. The experiment's web setup allowed to collect 101 similarity ratings and to differentiate native and non-native English speakers. Such a large and diverse dataset enables to confidently evaluate similarity metrics by correlating them with human assessments. Experimental evaluations using WordNet indicate that the proposed metric, coupled with the notion of intrinsic IC, yields results above the state of the art. Moreover, the intrinsic IC formulation also improves the accuracy of other IC-based metrics. In order to investigate the generality of both the intrinsic IC formulation and proposed similarity metric a further evaluation using the MeSH biomedical ontology has been performed. Even in this case significant results were obtained. The proposed metric and several others have been implemented in the Java WordNet Similarity Library.
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paper_title: An approach for measuring semantic similarity between words using multiple information sources

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Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.
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paper_title: Intelligent Indexing and Semantic Retrieval of Multimodal Documents

paper_content:

Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimedia information retrieval systems (MMIR). This research addresses the problem of finding pictures. The fact that images do not appear in isolation, but rather with accompanying, collateral text is exploited. Taken independently, existing techniques for picture retrieval using (i) text-based and (ii) image-based methods have several limitations. This research presents a general model for multimodal information retrieval that addresses the following issues: (i) users' information need, (ii) expressing information need through composite, multimodal queries, and (iii) determining the most appropriate weighted combination of indexing techniques in order to best satisfy information need. A machine learning approach is proposed for the latter. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision.
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paper_title: A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

paper_content:

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.
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paper_title: Using measures of semantic relatedness for word sense disambiguation

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This paper generalizes the Adapted Lesk Algorithm of Banerjee and Pedersen (2002) to a method of word sense disambiguation based on semantic relatedness. This is possible since Lesk's original algorithm (1986) is based on gloss overlaps which can be viewed as a measure of semantic relatedness. We evaluate a variety of measures of semantic relatedness when applied to word sense disambiguation by carrying out experiments using the English lexical sample data of SENSEVAL-2. We find that the gloss overlaps of Adapted Lesk and the semantic distance measure of Jiang and Conrath (1997) result in the highest accuracy.
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paper_title: Concept similarity in Formal Concept Analysis: An information content approach

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Abstract Formal Concept Analysis (FCA) is revealing interesting in supporting difficult activities that are becoming fundamental in the development of the Semantic Web. Assessing concept similarity is one of such activities since it allows the identification of different concepts that are semantically close. In this paper, a method for measuring the similarity of FCA concepts is presented, which is a refinement of a previous proposal of the author. The refinement consists in determining the similarity of concept descriptors (attributes) by using the information content approach, rather than relying on human domain expertise. The information content approach which has been adopted allows a higher correlation with human judgement than other proposals for evaluating concept similarity in a taxonomy defined in the literature.
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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: A Knowledge-Based Question Answering System for B2C eCommerce

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The evolution of B2C eCommerce has been formed various generations. Last models of B2C eCommerce are comparative shopping systems that connect to multiple vendors' databases and collect the information requested by the user. The comparative result obtained is then displayed in a tabular format in the user's browser. Although this scenario is much better than the multiple manual site comparisons, user still needs to face inconsistent user interfaces when he is linked from the comparison site to the actual purchasing site for shopping. Therefore, user has to learn logics of each site's user interface. In this paper, we propose a question answering system based on natural language processing techniques for retail (B2C) in eCommerce. This system gets a question in natural language formats, decomposes it to keywords, and extracts constraints automatically. Corresponding answers are then retrieved from the vendors' Web sites by exploiting the question constraints.
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paper_title: Development and application of a metric on semantic nets

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Motivated by the properties of spreading activation and conceptual distance, the authors propose a metric, called distance, on the power set of nodes in a semantic net. Distance is the average minimum path length over all pairwise combinations of nodes between two subsets of nodes. Distance can be successfully used to assess the conceptual distance between sets of concepts when used on a semantic net of hierarchical relations. When other kinds of relationships, like 'cause', are used, distance must be amended but then can again be effective. The judgements of distance significantly correlate with the distance judgements that people make and help to determine whether one semantic net is better or worse than another. The authors focus on the mathematical characteristics of distance that presents novel cases and interpretations. Experiments in which distance is applied to pairs of concepts and to sets of concepts in a hierarchical knowledge base show the power of hierarchical relations in representing information about the conceptual distance between concepts. >
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paper_title: Discovering implicit intention-level knowledge from natural-language texts

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In this paper, we propose a new approach to automatic discovery of implicit rhetorical information from texts based on evolutionary computation methods. In order to guide the search for rhetorical connections from natural-language texts, the model uses previously obtained training information which involves semantic and structural criteria. The main features of the model and new designed operators and evaluation functions are discussed, and the different experiments assessing the robustness and accuracy of the approach are described. Experimental results show the promise of evolutionary methods for rhetorical role discovery.
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paper_title: A New Method for Calculating Word Sense Similarity in WordNet 1

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Semantic similarity between word senses is hot topic in many applications of computational linguistics and artificial intelligence, such as word sense disambiguation, information extraction, semantic annotation and ontology learning. Many methods for calculating word sense similarity have been proposed. In recent years the methods based on WordNet have shown its talents and attracted great concern. In the paper, we present a new method in WordNet for calculating word sense similarity, which is noun and is-a relation based. We evaluate our method on the data set of Rubenstein and Goodenough, which is traditional and widely used. The correlation with human judgment is o.8804 in proposed measure, which is more close to human judgments than related works. Experiments show that our new measure significantly outperformed than other existing computational methods.
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paper_title: WordNet: An Electronic Lexical Database

paper_content:

A teaching device to acquaint dental students and also patients with endodontic root canal techniques performed by dentists and utilizing an electronic oscillator having a scale reading in electric current measurement and a pair of electrical circuit conductors being connected at one end to the terminals of the oscillator and the opposite ends thereof respectively being connectable to one or more small diameter metal wires which simulate dental reamers and files which are movable in root canal-simulating passages of uniform diameter complementary to that of said wires and formed in a transparent model of a human tooth including a root and cusp thereon and mounted in a transparent enclosure in which the root portion of the tooth extends with the cusp of the model extending above the upper end of the enclosure.
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paper_title: An Intrinsic Information Content Metric for Semantic Similarity in WordNet

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Information Content (IC) is an important dimension of word knowledge when assessing the similarity of two terms or word senses. The conventional way of measuring the IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with statistics on their actual usage in text as derived from a large corpus. In this paper we present a wholly intrinsic measure of IC that relies on hierarchical structure alone. We report that this measure is consequently easier to calculate, yet when used as the basis of a similarity mechanism it yields judgments that correlate more closely with human assessments than other, extrinsic measures of IC that additionally employ corpus analysis.
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paper_title: Intelligent Indexing and Semantic Retrieval of Multimodal Documents

paper_content:

Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimedia information retrieval systems (MMIR). This research addresses the problem of finding pictures. The fact that images do not appear in isolation, but rather with accompanying, collateral text is exploited. Taken independently, existing techniques for picture retrieval using (i) text-based and (ii) image-based methods have several limitations. This research presents a general model for multimodal information retrieval that addresses the following issues: (i) users' information need, (ii) expressing information need through composite, multimodal queries, and (iii) determining the most appropriate weighted combination of indexing techniques in order to best satisfy information need. A machine learning approach is proposed for the latter. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision.
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paper_title: New model of semantic similarity measuring in wordnet

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This paper presents a new model of measuring semantic similarity in the taxonomy of WordNet. The model takes the path length between two concepts and IC value of each concept as its metric, furthermore, the weight of two metrics can be adapted artificially. In order to evaluate our model, traditional and widely used datasets are used. Firstly, coefficients of correlation between human ratings of similarity and six computational models are calculated, the result shows our new model outperforms their homologues. Then, the distribution graphs of similarity value of 65 word pairs are discussed our model having no faulted zone more centralized than other five methods. So our model can make up the insufficient of other methods which only using one metric(path length or IC value) in their model.
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paper_title: Ontology-based information content computation

paper_content:

The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concept's semantics. As a result, IC has been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of appearance of concepts in corpora. However, the applicability and scalability of this method are hampered due to corpora dependency and data sparseness. More recently, some authors proposed IC-based measures using taxonomical features extracted from an ontology for a particular concept, obtaining promising results. In this paper, we analyse these ontology-based approaches for IC computation and propose several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept. Our approach has been evaluated and compared with related works (both corpora and ontology-based ones) when applied to the task of semantic similarity estimation. Results obtained for a widely used benchmark show that our method enables similarity estimations which are better correlated with human judgements than related works.
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paper_title: Intelligent Indexing and Semantic Retrieval of Multimodal Documents

paper_content:

Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimedia information retrieval systems (MMIR). This research addresses the problem of finding pictures. The fact that images do not appear in isolation, but rather with accompanying, collateral text is exploited. Taken independently, existing techniques for picture retrieval using (i) text-based and (ii) image-based methods have several limitations. This research presents a general model for multimodal information retrieval that addresses the following issues: (i) users' information need, (ii) expressing information need through composite, multimodal queries, and (iii) determining the most appropriate weighted combination of indexing techniques in order to best satisfy information need. A machine learning approach is proposed for the latter. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision.
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paper_title: Metric of intrinsic information content for measuring semantic similarity in an ontology

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Measuring information content (IC) from the intrinsic information of an ontology is an important however a formidable task. IC is useful for further measurement of the semantic similarity. Although the state-of-art metrics measure IC, they deal with external knowledge base or intrinsic hyponymy relations only. A current complex form of ontology conceptualizes a class (also often called as a concept) explicitly with the help of the hyponymy classes and the asserted relations and restrictions. Therefore, we propose a modified metric for measuring IC intrinsically taking both the concept-to-concept and the concept-to-property relations. We evaluate our system theoretically and with experimental data. Our evaluation shows the effectiveness of our modified metric for extracting intrinsic information content to measure semantic similarity among concepts in an ontology.
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Title: Obtaining Information Content of Concepts: An Overview

Section 1: Introduction
Description 1: Provide an introductory overview about the importance of semantic similarity measures between concepts, particularly in computational linguistics and artificial intelligence, and introduce the general objectives of the paper.

Section 2: WordNet
Description 2: Explain WordNet, highlighting its structure, purpose, and role in creating semantic networks for English words.

Section 3: Corpus-dependent IC Model
Description 3: Discuss the approach and methodology of corpus-dependent information content models, detailing how IC values are derived from statistical analysis of corpora.

Section 4: Corpus-independent IC Model
Description 4: Present various corpus-independent IC models, explaining how they derive IC without relying on external corpora.

Section 5: Hyponym-based IC Model
Description 5: Discuss the hyponym-based IC model, elaborating on methodologies proposed by Nuno and Zhou, and covering how IC is inferred from hyponym relations in the taxonomy.

Section 6: Concepts' Topology-based IC Model
Description 6: Detail the concepts' topology-based IC model, focusing on the methodology that considers each concept's topology including its depth and sibling concepts.

Section 7: Leaves-based IC Model
Description 7: Explain the leaves-based IC model proposed by David, emphasizing how IC is calculated based on the number of leaves and the concept’s depth in the taxonomy.

Section 8: Relation-based IC Model
Description 8: Describe the relation-based IC model, discussing how it incorporates the concept's properties, restrictions, and other logical assertions into the IC calculation.

Section 9: Discussion and Further Research
Description 9: Summarize the different IC models, compare their characteristics, discuss the limitations and strengths of each model, and suggest areas for further research.

Section 10: Summary
Description 10: Provide an overall summary of the paper, recapitulating the various methods of computing information content, comparing the models, and outlining important issues and future directions.