Synonym extraction using a semantic distance on a dictionary

Muller, Philippe; Hathout, Nabil; Gaume, Bruno

doi:10.3115/1654758.1654773

Cited by 19 publications

(19 citation statements)

References 18 publications

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“…More popular applications of text graphs include building and studying dictionary graphs (of semantically-related senses), which are then used for automatic synonym extraction (Blondel et al 2004;Ho and Fairon 2004;Muller et al 2006), or word sense disambiguation (Agirre and Soroa 2009;Gaume 2008;Kozima 1993;Mihalcea and Tarau 2004;Véronis and Ide 1990). In such graphs, topological properties are interpreted as indicators of dictionary quality or consistency (Sigman and Cecchi 2002).…”

Section: Text As Graphmentioning

confidence: 99%

Graph-based term weighting for information retrieval

Blanco

Lioma²

2011

Inf Retrieval

168

142

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A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures.

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Section: Text As Graphmentioning

confidence: 99%

Graph-based term weighting for information retrieval

Blanco

Lioma²

2011

Inf Retrieval

168

142

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“…The approach mixes gloss analysis as well as the analysis of WordNet structure. Interested readers may also consider (Li et al, 2003;Patwardhan et al, 2003;Banerjee and Pedersen, 2003;Patwardhan and Pedersen, 2006;Muller et al, 2006).…”

Section: Generalitiesmentioning

confidence: 99%

“…Examples of measures based on random walk techniques are defined and discussed in (Muller et al, 2006;Hughes and Ramage, 2007;Ramage et al, 2009;Fouss et al, 2007;Garla and Brandt, 2012).…”

Section: Random Walk Approachesmentioning

confidence: 99%

“…In other cases a graph representation of a corpus of texts will be build and used as a semantic proxy. For instance, Muller et al (2006) analyse a dictionary to generate a graph in which nodes are entries in the dictionary or words which occurs in definitions. An edge is created between an entry and each word in its definition.…”

Section: Other Corpus-based Measuresmentioning

confidence: 99%

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Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

157

102

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Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

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“…The vertexes of the graph are words of the dictionary and an edge from vertex a to vertex b shows that word b appears in the definition of a. In [7] the graph structure of a dictionary is considered as a Markov chain whose states are the graph nodes and whose transitions are its edges, valuated with probabilities. Then the distance between words is used to isolate candidate synonyms for a given word.…”

Section: Introductionmentioning

confidence: 99%

Automatic Acquisition of Synonyms of Verbs from an Explanatory Dictionary Using Hyponym and Hyperonym Relations

Castro-Sánchez

Sidorov

2011

Lecture Notes in Computer Science

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Abstract. In this paper we present an automatic method for extraction of synonyms of verbs from an explanatory dictionary based only on hyponym/hyperonym relations existing between the verbs defined and the genus used in their definitions. The set of pairs verb-genus can be considered as a directed graph, so we applied an algorithm to identify cycles in these kind of structures. We found that some cycles represent chains of synonyms. We obtain high precision and low recall.

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Synonym extraction using a semantic distance on a dictionary

Cited by 19 publications

References 18 publications

Graph-based term weighting for information retrieval

Graph-based term weighting for information retrieval

Semantic Similarity from Natural Language and Ontology Analysis

Automatic Acquisition of Synonyms of Verbs from an Explanatory Dictionary Using Hyponym and Hyperonym Relations

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