Named Entity Recognition with Bilingual Constraints
Wanxiang Che, Mengqiu Wang, Chris Manning and Ting Liu
Different languages contain complementary cues about entities,
which can be used to improve Named Entity Recognition (NER) systems.
We propose a method that formulates the problem of exploring such
signals on unannotated bilingual text as a simple Integer Linear Program,
which encourages entity tags to agree via bilingual constraints.
Bilingual NER experiments on the large OntoNotes 4.0 Chinese-English corpus
that the proposed method can improve strong baselines for both Chinese and
In particular, Chinese performance improves by over 5\% absolute F$_1$ score.
We can then annotate a large amount of bilingual text (80k sentence pairs)
using our method,
and add it as up-training data to the original monolingual NER training corpus.
The Chinese model retrained on this new combined dataset outperforms
the strong baseline by over 3\% F$_1$ score.
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