Chinese Word Segmentation and Named Entity Recognition Based on Conditional Random Fields Models
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Summary
A Chinese named entity recognition system NER@ISCAS is described, which integrates text, part-of-speech and a small-vocabularycharacter-lists feature for MSRA NER open track under the framework of Conditional Random Fields (CRFs) model.
- Type
- article
- Published
- 2006-07-01
- Cited by
- 19
- References
- 11
- OpenAlex
- https://openalex.org/W45206245
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2046748
Keywords
Conditional random field, CRFS, Named-entity recognition, Computer science, Artificial intelligence
References
- Using Non-Local Features to Improve Named Entity Recognition Recall
- Chinese Part-of-Speech Tagging: One-at-a-Time or All-at-Once? Word-Based or Character-Based?
- Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
- Minority Vote: At-Least-N Voting Improves Recall for Extracting Relations
- An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition
- Collective Information Extraction with Relational Markov Networks
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
- The Third International Chinese Language Processing Bakeoff: Word Segmentation and Named Entity Recognition
- Early results for Chinese named entity recognition using conditional random fields model, HMM and maximum entropy
Cited by
- Recognizing named entities in biomedical texts
- Domain Adaptation for Conditional Random Fields
- User recommendation implementation in the search engine based on Ajax
- User Recommendation Implementation in the Search Engine Based on Ajax
- Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese
- The Effect of Answer Patterns for Supervised Named Entity Recognition in Thai
- Deep Learning for Chinese Word Segmentation and POS Tagging
- Two Step Chinese Named Entity Recognition Based on Conditional Random Fields Models
- An Encoding Strategy Based Word-Character LSTM for Chinese NER
- An Attention-Based BILSTM-CRF for Chinese Named Entity Recognition
- Dual Neural Network Fusion Model for Chinese Named Entity Recognition
- A Novel Character-Word Fusion Chinese Named Entity Recognition Model Based on Attention Mechanism
- Exploiting Character-Word Fusion to Enhance Chinese Named Entity Recognition Combined with Multi-head Attention Mechanism
- Boundary-Aware Abstractive Summarization with Entity-Augmented Attention for Enhancing Faithfulness
- What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models
- MISS: Multiple information span scoring for Chinese named entity recognition
- Recognizing Biomedical Named Entities in Chinese Research Abstracts
- ii Preface
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