Improving Chinese Word Segmentation with Description Length Gain
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Summary
A novel approach to CWS is presented that utilizes description length gain (DLG), an empirical goodness measure for unsupervised word discovery, to enhancethe segmentation performance of conditional random (CRF) learning.
- Type
- article
- Published
- 2007-01-01
- Cited by
- 4
- References
- 31
- OpenAlex
- https://openalex.org/W2864082
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14704542
Keywords
Computer science, Artificial intelligence, Conditional random field, Segmentation, Natural language processing
References
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- A Systematic Cross-Comparison of Sequence Classifiers
- Chinese Word Segmentation with Maximum Entropy and N-gram Language Model
- Information Theory: 1948-1998 - Guest Editorial
- Unsupervised Learning of Word Boundary with Description Length Gain
- Stochastic Complexity in Statistical Inquiry
- Unsupervised lexical learning as inductive inference.
- PAT-tree-based keyword extraction for Chinese information retrieval
- Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus
- Extraction of Chinese Compound Words - An Experimental Study on a Very Large Corpus
- The First International Chinese Word Segmentation Bakeoff
- Chinese Segmentation and New Word Detection using Conditional Random Fields
- Modeling By Shortest Data Description*
- An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
- Contextual Dependencies in Unsupervised Word Segmentation
- Chinese Word Segmentation without Using Lexicon and Hand-crafted Training Data
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
- Chinese Word Segmentation as Character Tagging
- Effective Tag Set Selection in Chinese Word Segmentation via Conditional Random Field Modeling
- Chinese Word Segmentation and Named Entity Recognition Based on a Context-Dependent Mutual Information Independence Model
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