Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
Abstract
Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings. For languages where word boundaries are not readily identified in text, word segmentation is a key first step to generating features for an NER system. While using word boundary tags as features are helpful, the signals that aid in identifying these boundaries may provide richer information for an NER system. New state-of-the-art word segmentation systems use neural models to learn representations for predicting word boundaries. We show that these same representations, jointly trained with an NER system, yield significant improvements in NER for Chinese social media. In our experiments, jointly training NER and word segmentation with an LSTM-CRF model yields nearly 5% absolute improvement over previously published results.
Cite
@article{arxiv.1603.00786,
title = {Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning},
author = {Nanyun Peng and Mark Dredze},
journal= {arXiv preprint arXiv:1603.00786},
year = {2017}
}
Comments
This is the camera ready version of our ACL'16 paper. We also added a supplementary material containing the results of our systems on a cleaner dataset (much higher F1 scores). More information please refer to the repo https://github.com/hltcoe/golden-horse