Inducing a semantically annotated lexicon via EM-based clustering

Rooth, Mats; Riezler, Stefan; Prescher, Detlef; Carroll, Glenn R.; Beil, Franz

doi:10.3115/1034678.1034703

Cited by 80 publications

(130 citation statements)

References 9 publications

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“…K-means is a standard flat, hard-clustering algorithm; we used the Weka implementation (Witten and Frank, 2005). LSC (Rooth, 1998;Rooth et al, 1999) is a two-dimensional soft-clustering algorithm which learns three probability distributions: one for the clusters, and one for the output probabilities of each element and for each feature type with regard to a cluster. The latter two (elements and features) correspond to the two dimensions of the clustering.…”

Section: Clustering Methodsmentioning

confidence: 99%

Syntactic Transfer Patterns of German Particle Verbs and their Impact on Lexical Semantics

Bott

Walde

2014

Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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German particle verbs, like anblicken (to gaze at) combine a base verb (blicken) with a particle (an) to form a special kind of Multi Word Expression. Particle verbs may share the semantics of the base verb and the particle to a variable degree. However, while syntactic subcategorization frames tend to be good predictor for the semantics of verbs in general (verbs that are similar in meaning also tend to have similar subcategorization frames and selectional preferences), there are regular changes in subcategorization frames by particle verbs with regard to the corresponding base verbs. This paper demonstrates that the syntactic behavior of particle verbs and base verbs together (modeling regular changes in subcategorization frames by particle verbs and corresponding base verbs) and applying clustering techniques allows us to distinguish particle verb meaning and shows the tight connection between transfer patterns and the semantic classes of particle verbs.

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Section: Clustering Methodsmentioning

confidence: 99%

Syntactic Transfer Patterns of German Particle Verbs and their Impact on Lexical Semantics

Bott

Walde

2014

Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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“…That can be useful in real WSD. Others who have worked on variations of PWSD include Gale et al (1992); Schütze (1998); Lee (1999); Dagan et al (1999); Rooth et al (1999); Clark and Weir (2002); Weeds and Weir (2005); ZhitomirskyGeffet and Dagan (2009). The methodology we followed was similar to that of Weeds and Weir.…”

Section: Pseudo-word-sense Disambiguationmentioning

confidence: 99%

Evaluation of automatic updates of Roget’s Thesaurus

Kennedy

Śzpakowicz

2014

JLM

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Thesauri and similarly organised resources attract increasing interest of Natural Language Processing researchers. Thesauri age fast, so there is a constant need to update their vocabulary. Since a manual update cycle takes considerable time, automated methods are required. This work presents a tuneable method of measuring semantic relatedness, trained on Roget's Thesaurus, which generates lists of terms related to words not yet in the Thesaurus. Using these lists of terms, we experiment with three methods of adding words to the Thesaurus. We add, with high confidence, over 5500 and 9600 new word senses to versions of Roget's Thesaurus from 1911 and 1987 respectively. We evaluate our work both manually and by applying the updated thesauri in three NLP tasks: selection of the best synonym from a set of candidates, pseudo-word-sense disambiguation and SAT-style analogy problems. We find that the newly added words are of high quality. The additions significantly improve the performance of Roget's-based methods in these NLP tasks. The performance of our system compares favourably with that of WordNet-based methods. Our methods are general enough to work with different versions of Roget's Thesaurus.

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“…In recent years, a number of approaches have been proposed for dealing computationally with selectional preference acquisition (Resnik (1996); Briscoe and Carroll (1997); McCarthy (1997); Rooth et al (1999); Abney and Light (1999); Ciaramita and Johnson (2000); Korhonen (2002)). …”

Section: Selectional Preference Acquisitionmentioning

confidence: 99%

Automated induction of sense in context

Pustejovsky

Hanks

Rumshisky

2004

Proceedings of the 20th International Conference on Computational Linguistics - COLING '04

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In this paper, we introduce a model for sense assignment which relies on assigning senses to the contexts within which words appear, rather than to the words themselves. We argue that word senses as such are not directly encoded in the lexicon of the language. Rather, each word is associated with one or more stereotypical syntagmatic patterns, which we call selection contexts. Each selection context is associated with a meaning, which can be expressed in any of various formal or computational manifestations. We present a formalism for encoding contexts that help to determine the semantic contribution of a word in an utterance. Further, we develop a methodology through which such stereotypical contexts for words and phrases can be identified from very large corpora, and subsequently structured in a selection context dictionary, encoding both stereotypical syntactic and semantic information. We present some preliminary results.

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Inducing a semantically annotated lexicon via EM-based clustering

Cited by 80 publications

References 9 publications

Syntactic Transfer Patterns of German Particle Verbs and their Impact on Lexical Semantics

Syntactic Transfer Patterns of German Particle Verbs and their Impact on Lexical Semantics

Evaluation of automatic updates of Roget’s Thesaurus

Automated induction of sense in context

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