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Chinese Word Segmentation

Chinese word segmentation is the task of splitting Chinese text (i.e. a sequence of Chinese characters) into words (Source: www.nlpprogress.com).

Papers

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Showing 151–200 of 244 papers

TitleStatusHype
Long Short-Term Memory Neural Networks for Chinese Word Segmentation—0
A Transition-based Model for Joint Segmentation, POS-tagging and Normalization—0
Semi-supervised Chinese Word Segmentation based on Bilingual Information—0
Accurate Linear-Time Chinese Word Segmentation via Embedding Matching—0
Coupled Sequence Labeling on Heterogeneous Annotations: POS Tagging as a Case Study—0
Chinese Spelling Check System Based on N-gram Model—0
Create a Manual Chinese Word Segmentation Dataset Using Crowdsourcing Method—0
The Discovery of Natural Typing Annotations: User-produced Potential Chinese Word Delimiters—0
Gated Recursive Neural Network for Chinese Word Segmentation—0
Inducing Word and Part-of-Speech with Pitman-Yor Hidden Semi-Markov Models—0
Radical Embedding: Delving Deeper to Chinese Radicals—0
NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit—0
Synthetic Word Parsing Improves Chinese Word Segmentation—0
Overview of the NLPCC 2015 Shared Task: Chinese Word Segmentation and POS Tagging for Micro-blog Texts—0
Automatic Adaptation of Annotations—0
Semi-Supervised Chinese Word Segmentation Using Partial-Label Learning With Conditional Random Fields—0
Joint Learning of Chinese Words, Terms and Keywords—0
A Joint Model for Unsupervised Chinese Word Segmentation—0
A mixed approach for Chinese word segmentation—0
The CIPS-SIGHAN CLP 2014 Chinese Word Segmentation Bake-off—0
Domain Adaptation for CRF-based Chinese Word Segmentation using Free Annotations—0
Predicting Chinese Abbreviations with Minimum Semantic Unit and Global Constraints—0
Exploring Mental Lexicon in an Efficient and Economic Way: Crowdsourcing Method for Linguistic Experiments—0
Automatic Corpus Expansion for Chinese Word Segmentation by Exploiting the Redundancy of Web Information—0
Chinese Morphological Analysis with Character-level POS Tagging—0
Effective Document-Level Features for Chinese Patent Word Segmentation—0
Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora—0
Max-Margin Tensor Neural Network for Chinese Word Segmentation—0
Character-Level Chinese Dependency Parsing—0
New Word Detection for Sentiment Analysis—0
Omni-word Feature and Soft Constraint for Chinese Relation Extraction—0
Toward Better Chinese Word Segmentation for SMT via Bilingual Constraints—0
Two Knives Cut Better Than One: Chinese Word Segmentation with Dual Decomposition—0
Automatic Refinement of Syntactic Categories in Chinese Word Structures—0
Why Chinese Web-as-Corpus is Wacky? Or: How Big Data is Killing Chinese Corpus Linguistics—0
Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese—0
Parsing Chinese Synthetic Words with a Character-based Dependency Model—0
Radical-Enhanced Chinese Character Embedding—0
Regularized Structured Perceptron: A Case Study on Chinese Word Segmentation, POS Tagging and Parsing—0
Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging—0
Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields—0
A Study of the Effectiveness of Suffixes for Chinese Word Segmentation—0
Context-Based Chinese Word Segmentation using SVM Machine-Learning Algorithm without Dictionary Support—0
Can MDL Improve Unsupervised Chinese Word Segmentation?—0
A Common Case of Jekyll and Hyde: The Synergistic Effect of Using Divided Source Training Data for Feature Augmentation—0
Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
An Empirical Study Of Semi-Supervised Chinese Word Segmentation Using Co-Training—0
Human-Computer Interactive Chinese Word Segmentation: An Adaptive Dirichlet Process Mixture Model Approach—0
Joint Chinese Word Segmentation and POS Tagging on Heterogeneous Annotated Corpora with Multiple Task Learning—0
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