Neural Reranking for Named Entity Recognition

RANLP 2017  ·  Jie Yang, Yue Zhang, Fei Dong ·

We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions... In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark. read more

PDF Abstract RANLP 2017 PDF RANLP 2017 Abstract

Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Named Entity Recognition CoNLL 2003 (English) Yang et al. ([2017a]) F1 91.62 # 43

Methods