Name
Abstract | ||
|---|---|---|
We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language. We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations. As commonly done in the monolingual setting, we assume a worde entails a wordf if the prominent context features of e are a subset of those of f . To address the challenge of comparing contexts across languages, we propose a novel method for inducing sparse bilingual word representations from monolingual and parallel texts. Our approach yields an Fscore of 70%, and significantly outperforms strong baselines based on translation and on existing word representations. |
Year | Venue | Field |
|---|---|---|
2016 | HLT-NAACL | Logical consequence,Cross lingual,Computer science,Speech recognition,Artificial intelligence,Natural language processing |
DocType | Citations | PageRank |
Conference | 6 | 0.41 |
References | Authors | |
41 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yogarshi Vyas | 1 | 50 | 5.42 |
| Marine Carpuat | 2 | 587 | 51.99 |