2 research outputs found
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
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.Comment: 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-hors
Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text
Chinese word segmentation is necessary to provide word-level information for
Chinese named entity recognition (NER) systems. However, segmentation error
propagation is a challenge for Chinese NER while processing colloquial data
like social media text. In this paper, we propose a model (UIcwsNN) that
specializes in identifying entities from Chinese social media text, especially
by leveraging ambiguous information of word segmentation. Such uncertain
information contains all the potential segmentation states of a sentence that
provides a channel for the model to infer deep word-level characteristics. We
propose a trilogy (i.e., candidate position embedding -> position selective
attention -> adaptive word convolution) to encode uncertain word segmentation
information and acquire appropriate word-level representation. Experiments
results on the social media corpus show that our model alleviates the
segmentation error cascading trouble effectively, and achieves a significant
performance improvement of more than 2% over previous state-of-the-art methods.Comment: SocialNLP@ACL202