Representation of word meaning has been a topic of considerable debate within the field of computational linguistics, and particularly in the subfield of word sense disambiguation. While word senses enumerated in manually produced inventories have been very useful as a start point to research, we know that the inventory should be selected for the purposes of the application. Unfortunately we have no clear understanding of how to determine the appropriateness of an inventory for monolingual applications, or when the target language is unknown in cross-lingual applications. In this paper we examine datasets which have paraphrases or translations as alternative annotations of lexical meaning on the same underlying corpus data. We demonstrate that overlap in lexical paraphrases (substitutes) between two uses of the same lemma correlates with overlap in translations. We compare the degree of overlap with annotations of usage similarity on the same data and show that the overlaps in paraphrases or translations also correlate with the similarity judgements. This bodes well for using any of these methods to evaluate unsupervised representations of lexical semantics. We do however find that the relationship breaks down for some lemmas, but this behaviour on a lemma by lemma basis itself correlates with low inter-tagger agreement and higher proportions of mid-range points on a usage similarity dataset. Lemmas which have many inter-related usages might potentially be predicted from such data.
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- Measuring Similarity of Word Meaning in Context with Lexical Substitutes and Translations
- Springer Berlin Heidelberg
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