Named entity words tend to lie in a single hypersphere in word embedding space, and this geometric model can be transferred across languages and used as an auxiliary feature for NER.
Cross-Lingual Syntactic Transfer with Limited Resources
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abstract
We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available. The method makes use of three steps: 1) a method for deriving cross-lingual word clusters, which can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins (2015). Experiments show improvements over the state-of-the-art in several languages used in previous work, in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work. Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015). We conclude with results on 38 datasets from the Universal Dependencies corpora.
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cs.CL 1years
2019 1verdicts
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Modeling Named Entity Embedding Distribution into Hypersphere
Named entity words tend to lie in a single hypersphere in word embedding space, and this geometric model can be transferred across languages and used as an auxiliary feature for NER.