Measures of distributional similarity

Lee, Lillian

doi:10.3115/1034678.1034693

Cited by 381 publications

(308 citation statements)

References 31 publications

Supporting

Mentioning

296

Contrasting

Unclassified

Order By: Relevance

“…The problem arises when the probability of word combinations that do not occur in the training data needs to be estimated. The smoothing methods proposed in the literature (overviews are provided by Dagan et al (1999) and Lee (1999)) can be generally divided into three types: discounting (Katz, 1987), class-based smoothing (Resnik, 1993;Brown et al, 1992;Pereira et al, 1993), and distance-weighted averaging (Grishman and Sterling, 1994;Dagan et al, 1999).…”

Section: Smoothing Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating smoothing algorithms against plausibility judgements

Lapata

Keller

McDonald

2001

Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01

View full text Add to dashboard Cite

Previous research has shown that the plausibility of an adjective-noun combination is correlated with its corpus co-occurrence frequency. In this paper, we estimate the co-occurrence frequencies of adjective-noun pairs that fail to occur in a 100 million word corpus using smoothing techniques and compare them to human plausibility ratings. Both class-based smoothing and distance-weighted averaging yield frequency estimates that are significant predictors of rated plausibility, which provides independent evidence for the validity of these smoothing techniques.

show abstract

Section: Smoothing Methodsmentioning

confidence: 99%

“…A key feature of this type of smoothing is the function which measures distributional similarity from cooccurrence frequencies. Several measures of distributional similarity have been proposed in the literature (Dagan et al, 1999;Lee, 1999). We used two measures, the Jensen-Shannon divergence and the confusion probability.…”

Section: Distance-weighted Averagingmentioning

confidence: 99%

Evaluating smoothing algorithms against plausibility judgements

Lapata

Keller

McDonald

2001

Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The motivation behind this design is to cover all the types of OWL hierarchical set relations, such as subclass, union, and intersection. Furthermore, this pattern relies on theoretical foundations, including Jaccard similarity coefficient [27], similarity in semantic networks by Rada et al [28], feature-based similarity in description logic by Borgida et al [29], and general cognitive theories about similarity by Tversky [30].…”

Section: Set Hierarchy Patternmentioning

confidence: 99%