Multilingual extraction and categorization of lexical collocations with graph-aware transformers
arXiv preprint arXiv:2205.11456, 2022•arxiv.org
Recognizing and categorizing lexical collocations in context is useful for language learning,
dictionary compilation and downstream NLP. However, it is a challenging task due to the
varying degrees of frozenness lexical collocations exhibit. In this paper, we put forward a
sequence tagging BERT-based model enhanced with a graph-aware transformer
architecture, which we evaluate on the task of collocation recognition in context. Our results
suggest that explicitly encoding syntactic dependencies in the model architecture is helpful …
dictionary compilation and downstream NLP. However, it is a challenging task due to the
varying degrees of frozenness lexical collocations exhibit. In this paper, we put forward a
sequence tagging BERT-based model enhanced with a graph-aware transformer
architecture, which we evaluate on the task of collocation recognition in context. Our results
suggest that explicitly encoding syntactic dependencies in the model architecture is helpful …
Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP. However, it is a challenging task due to the varying degrees of frozenness lexical collocations exhibit. In this paper, we put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
arxiv.org
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