Making sense of collocations

Wanner, Leo; Bohnet, Bernd; Giereth, Mark

doi:10.1016/j.csl.2005.10.002

Cited by 19 publications

(21 citation statements)

References 23 publications

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“…This is significantly higher than the previously reported result of 0.740 for f-measure, though the comparison is not fair because we looked for the meaning which is similar to the meaning predicted in [17], but not the same one. The highest f-measure achieved in the experiments on an independent test set was only 0.658.…”

Section: Discussioncontrasting

confidence: 50%

“…The best state-of-the-art result for predicting a lexical function with the meaning 'cause' is f-measure of 0.76 given by ID3 algorithm [17]. In our experiments, the best f-measure of 0.877 was shown by PART for the meaning MAKE.…”

Section: Experiments On the Training Setsmentioning

confidence: 74%

“…Collocations may have different syntactic structures, and the verb-noun pattern is one of these structures. L.Wanner [16,17] made experiments to classify Spanish verb-noun pairs according to nine lexical functions with the meaning 'perform, experience, carry out something, 'cause the existence of something, 'begin to perform something, 'continue to perform something', etc. Verb-noun pairs were divided in two groups.…”

Section: Related Workmentioning

confidence: 99%

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Supervised Machine Learning for Predicting the Meaning of Verb-Noun Combinations in Spanish

Kolesnikova

Gelbukh

2010

Advances in Soft Computing

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Abstract. The meaning of such verb-noun combinations as take care, undertake work, pay attention can be generalized as DO what is designated by the noun. Likewise, the meaning of make a decision, provide support, write a letter can be generalized as MAKE what is designated by the noun. These generalizations represent the meaning of certain groups of verb-noun combinations. We use supervised machine learning algorithms to predict the meanings DO, MAKE, BEGIN, and CONTINUE of previously unseen verbnoun pairs. We evaluate the performance of the applied algorithms on a training set using 10-fold cross-validation technique. The learnt models have also been evaluated on an independent test set and the predictions have been checked manually to determine the accuracy of the classifiers. The obtained results show that supervised machine learning methods achieve significant accuracy and can be used for semantic annotation of verb-noun combinations.

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Section: Discussioncontrasting

confidence: 50%

Section: Experiments On the Training Setsmentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

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Supervised Machine Learning for Predicting the Meaning of Verb-Noun Combinations in Spanish

Kolesnikova

Gelbukh

2010

Advances in Soft Computing

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“…Most of this work is based on statistical measures that indicate how likely the elements of a possible collocation are to co-occur, while ignoring the semantics of the collocations. Semantic classification of collocations has been addressed, for instance, in (Wanner et al, 2006;Gelbukh and Kolesnikova., 2012;Moreno et al, 2013;. However, to the best of our knowledge, our work is the first to automatically retrieve and typify collocations simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Gurevych

Miyao

2016

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“…A different perspective on collocation extraction focuses not only on their retrieval, but on semantically classifying the obtained collocations, in order to make them more useful for NLP applications (Wanner et al, 2006;Wanner et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Using bilingual word-embeddings for multilingual collocation extraction

García¹,

Salido²,

Alonso-Ramos³

2017

Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

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This paper presents a new strategy for multilingual collocation extraction which takes advantage of parallel corpora to learn bilingual word-embeddings. Monolingual collocation candidates are retrieved using Universal Dependencies, while the distributional models are then applied to search for equivalents of the elements of each collocation in the target languages. The proposed method extracts not only collocation equivalents with direct translation between languages, but also other cases where the collocations in the two languages are not literal translations of each other. Several experiments -evaluating collocations with three syntactic patterns-in English, Spanish, and Portuguese show that our approach can effectively extract large pairs of bilingual equivalents with an average precision of about 90%. Moreover, preliminary results on comparable corpora suggest that the distributional models can be applied for identifying new bilingual collocations in different domains.

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Making sense of collocations

Cited by 19 publications

References 23 publications

Supervised Machine Learning for Predicting the Meaning of Verb-Noun Combinations in Spanish

Supervised Machine Learning for Predicting the Meaning of Verb-Noun Combinations in Spanish

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Using bilingual word-embeddings for multilingual collocation extraction

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