Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.Comment: accepted at CIKM 201

Chinese Location Word Recognition Using Service Context Information for Location-Based Service

With the development of mobile networks and positioning technology, extensive attention focuses on the location-based service (LBS) which processes the application data including user queries, information searches, and user comments by the location information. In LBS, the recognition of the location word in user messages is meaningful and important. The location word recognition in LBS is different from the traditional named entity recognition, owing to the additional information such as user location coordinates in LBS. This paper proposes a method that adds the service context information including user location coordinates and message timestamps into the machine learning to improve the accuracy of the Chinese location word recognition. The experiment based on microblog datasets in mobile environment proves the viability and effectiveness of this method

Conditional random fields with dynamic potentials for Chinese named entity recognition.

Wu, Yiu Kei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (p. 69-75).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Chinese NER Problem --- p.1Chapter 1.2 --- Contribution of Our Proposed Framework --- p.3Chapter 2 --- Related Work --- p.6Chapter 2.1 --- Hidden Markov Models --- p.7Chapter 2.2 --- Maximum Entropy Models --- p.8Chapter 2.3 --- Conditional Random Fields --- p.10Chapter 3 --- Our Proposed Model --- p.14Chapter 3.1 --- Background --- p.14Chapter 3.1.1 --- Problem Formulation --- p.14Chapter 3.1.2 --- Conditional Random Fields --- p.16Chapter 3.1.3 --- Semi-Markov Conditional Random Fields --- p.26Chapter 3.2 --- The Formulation of Our Proposed Model --- p.28Chapter 3.2.1 --- The Main Principle --- p.28Chapter 3.2.2 --- The Detailed Formulation --- p.36Chapter 3.2.3 --- Adapting Features from Original CRF to CRFDP --- p.51Chapter 4 --- Experiments --- p.54Chapter 4.1 --- Datasets --- p.55Chapter 4.2 --- Features --- p.57Chapter 4.3 --- Evaluation Metrics --- p.61Chapter 4.4 --- Results and Discussion --- p.63Chapter 5 --- Conclusions and Future Work --- p.67Bibliography --- p.69A --- p.76B --- p.78C --- p.8