| Autor: |
Ziheng Zeng, Suma Bhat |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Transactions of the Association for Computational Linguistics, Vol 9, Pp 1546-1562 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2307-387X |
| DOI: |
10.1162/tacl_a_00442/108933/Idiomatic-Expression-Identification-using-Semantic |
| Popis: |
AbstractIdiomatic expressions are an integral part of natural language and constantly being added to a language. Owing to their non-compositionality and their ability to take on a figurative or literal meaning depending on the sentential context, they have been a classical challenge for NLP systems. To address this challenge, we study the task of detecting whether a sentence has an idiomatic expression and localizing it when it occurs in a figurative sense. Prior research for this task has studied specific classes of idiomatic expressions offering limited views of their generalizability to new idioms. We propose a multi-stage neural architecture with attention flow as a solution. The network effectively fuses contextual and lexical information at different levels using word and sub-word representations. Empirical evaluations on three of the largest benchmark datasets with idiomatic expressions of varied syntactic patterns and degrees of non-compositionality show that our proposed model achieves new state-of-the-art results. A salient feature of the model is its ability to identify idioms unseen during training with gains from 1.4% to 30.8% over competitive baselines on the largest dataset. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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