One Sense per Translation
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Abstract (arXiv)
Word sense disambiguation (WSD) is the task of determining the sense of a word in context. Translations have been used in WSD as a source of knowledge, and even as a means of delimiting word senses. In this paper, we define three theoretical properties of the relationship between senses and translations, and argue that they constitute necessary conditions for using translations as sense inventories. The key property of One Sense per Translation (OSPT) provides a foundation for a translation-based WSD method. The results of an intrinsic evaluation experiment indicate that our method achieves a precision of approximately 93% compared to manual corpus annotations. Our extrinsic evaluation experiments demonstrate WSD improvements of up to 4.6% F1-score on difficult WSD datasets.
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References · 13
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2013Exploiting Similarities among Languages for Machine Translationcited by 191
- 2020Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoderscited by 61
- 2019Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguationcited by 40
- 2019Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representationscited by 21
- 2021LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyondcited by 17
- 2021FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionarycited by 12
- 2017Handling Homographs in Neural Machine Translationcited by 11
- 2020Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguationcited by 4
- 2019One Homonym per Translationcited by 3
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- 2020Synonymy = Translational Equivalencecited by 3
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