Chinese Word Segmentation
48 papers with code • 6 benchmarks • 3 datasets
Chinese word segmentation is the task of splitting Chinese text (i.e. a sequence of Chinese characters) into words (Source: www.nlpprogress.com).
Benchmarks
These leaderboards are used to track progress in Chinese Word Segmentation
MSRA CN NER
Most implemented papers
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
CPF-NLPR/AT4ChineseNER
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EMNLP 2018
However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS.
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling
As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff.
Subword Encoding in Lattice LSTM for Chinese Word Segmentation
jiesutd/SubwordEncoding-CWS
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NAACL 2019
Previous lattice LSTM model takes word embeddings as the lexicon input, we prove that subword encoding can give the comparable performance and has the benefit of not relying on any external segmentor.
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
vatile/CWS-NAACL2019
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NAACL 2019
Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS.
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
fastnlp/JointCwsParser
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TACL 2020
Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing.
A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder
acphile/MCCWS
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Findings of the Association for Computational Linguistics 2020
Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion.
Attention Is All You Need for Chinese Word Segmentation
Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model.
Neural Chinese Word Segmentation as Sequence to Sequence Translation
In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework.
Improving Chinese Word Segmentation with Wordhood Memory Networks
Contextual features always play an important role in Chinese word segmentation (CWS).
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.
