Semantics-Driven Recognition of Collocations Using Word Embeddings

Rodríguez-Fernández, Sara; Espinosa-Anke, Luis; Carlini, Roberto; Wanner, Leo

doi:10.18653/v1/p16-2081

Cited by 14 publications

(13 citation statements)

References 17 publications

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“…During the annotation process, 447 combinations were marked as doubt, and out of these, 260 were finally considered collocations by the language experts. Even if we do not make explicit use of lexical functions in this paper, it is worth mentioning that the collocations in these final corpora are labeled using 60 LFs, which may be useful to evaluate extraction and classification strategies Rodríguez-Fernández et al, 2016;Kolesnikova and Gelbukh, 2018).…”

Section: Final Resources and Resultsmentioning

confidence: 99%

“…A related task consists of automatically classifying the semantic properties of collocations by means of lexical functions or glosses. In this regard, some studies apply machine learning methods to train classifiers (Wanner et al, 2006Gelbukh and Kolesnikova, 2012), while others use distributional semantics to identify a collocate given a base and a lexical function (Rodríguez-Fernández et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

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A comparison of statistical association measures for identifying dependency-based collocations in various languages.

García¹,

Salido²,

Alonso-Ramos³

2019

Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

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This paper presents an exploration of different statistical association measures to automatically identify collocations from corpora in English, Portuguese, and Spanish. To evaluate the impact of the association measures we manually annotated corpora with three different syntactic patterns of collocations (adjective-noun, verb-object and nominal compounds). We took advantage of the PARSEME 1.1 Shared Task corpora by selecting a subset of 155k tokens in the three referred languages, in which we annotated 1, 526 collocations with their Lexical Functions according to the Meaning-Text Theory. Using the resulting gold-standard, we have carried out a comparison between frequency data and several well-known association measures, both symmetric and asymmetric. The results show that the combination of dependency triples with raw frequency information is as powerful as the best association measures in most syntactic patterns and languages. Furthermore, and despite the asymmetric behaviour of collocations, directional approaches perform worse than the symmetric ones in the extraction of these phraseological combinations.

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Section: Final Resources and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A comparison of statistical association measures for identifying dependency-based collocations in various languages.

García¹,

Salido²,

Alonso-Ramos³

2019

Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

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show abstract

“…In the future, we would like to experiment with more data, so that enough training data can be obtained for less frequent LFs. To this end, we could benefit from the supervised approach proposed in (Rodríguez-Fernández et al, 2016), and then filter by pairwise correlation strength metrics such as PMI. Another exciting avenue would involve exploring cross-lingual transfer of LFs, taking advantage of recent development in unsupervised cross-lingual embedding learning (Artetxe et al, 2017;Conneau et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Collocation Classification with Unsupervised Relation Vectors

Espinosa-Anke¹,

Schockaert²,

Wanner³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold. First, we introduce a novel dataset with collocations categorized according to lexical functions. Second, we conduct experiments on a subset of this benchmark, comparing it in particular to the well known DiffVec dataset. In these experiments, in addition to simple word vector arithmetic operations, we also investigate the role of unsupervised relation vectors as a complementary input. While these relation vectors indeed help, we also show that lexical function classification poses a greater challenge than the syntactic and semantic relations that are typically used for benchmarks in the literature.

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“…While its main field of application is Information Retrieval, it has also been used in NLP tasks such as collocation recognition (Wu et al, 2010;Rodríguez-Fernández et al, 2016).…”

Section: Mean Reciprocal Rank (Mrr)mentioning

confidence: 99%

SemEval-2018 Task 9: Hypernym Discovery

Camacho-Collados¹,

Bovi²,

Espinosa-Anke³

et al. 2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as follows: given an input term, retrieve (or discover) its suitable hypernyms from a target corpus. We proposed five different subtasks covering three languages (English, Spanish, and Italian), and two specific domains of knowledge in English (Medical and Music). Participants were allowed to compete in any or all of the subtasks. Overall, a total of 11 teams participated, with a total of 39 different systems submitted through all subtasks. Data, results and further information about the task can be found at https://competitions. codalab.org/competitions/17119.

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Semantics-Driven Recognition of Collocations Using Word Embeddings

Cited by 14 publications

References 17 publications

A comparison of statistical association measures for identifying dependency-based collocations in various languages.

A comparison of statistical association measures for identifying dependency-based collocations in various languages.

Collocation Classification with Unsupervised Relation Vectors

SemEval-2018 Task 9: Hypernym Discovery

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