activity
20172022
most citedCombining Discrete and Neural Features for Sequence Labeling

36 citations · 58 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

7 papers · 1 filter

cs.CL2018

Subword Encoding in Lattice LSTM for Chinese Word Segmentation

Jie Yang, Yue Zhang, Shuailong Liang

We investigate a lattice LSTM network for Chinese word segmentation (CWS) to utilize words or subwords. It integrates the character sequence features with all subsequences informat…

cs.CL2018

Design Challenges and Misconceptions in Neural Sequence Labeling

Jie Yang, Shuailong Liang, Yue Zhang

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include…

cs.CL2018

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference l…

cs.CL2018

Chinese NER Using Lattice LSTM

Yue Zhang, Jie Yang

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with char…

cs.CL201736 cited

Combining Discrete and Neural Features for Sequence Labeling

Jie Yang, Zhiyang Teng, Meishan Zhang +1

Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural…

cs.CL20179 cited

Neural Reranking for Named Entity Recognition

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…