Lexical collocations are idiosyncratic combinations of two syntactically bound lexical items (e.g., “heavy rain”, “take a step” or “undergo surgery”). Understanding their degree of compositionality and idiosyncrasy, as well their underlying semantics, is crucial for language learners, lexicographers and downstream NLP applications alike. In this paper we analyse a suite of language models for collocation understanding. We first construct a dataset of apparitions of lexical collocations in context, ...
Lexical collocations are idiosyncratic combinations of two syntactically bound lexical items (e.g., “heavy rain”, “take a step” or “undergo surgery”). Understanding their degree of compositionality and idiosyncrasy, as well their underlying semantics, is crucial for language learners, lexicographers and downstream NLP applications alike. In this paper we analyse a suite of language models for collocation understanding. We first construct a dataset of apparitions of lexical collocations in context, categorized into 16 representative semantic categories. Then, we perform two experiments: (1) unsupervised collocate retrieval, and (2) supervised collocation classification in context. We find that most models perform well in distinguishing light verb constructions, especially if the collocation’s first argument acts as a subject, but often fail to distinguish, first, different syntactic structures within the same semantic category, and second, finer-grained categories which restrict the set of correct collocates.
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