Asian and Low-Resource Language Information Processing (TALLIP)


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ACM Transactions on Asian Language Information Processing (TALIP), Volume 5 Issue 4, December 2006

Introduction to special issue on reasoning in natural language information processing
Dawei Song, Jian-Yun Nie
Pages: 291-295
DOI: 10.1145/1236181.1236182

For any applications related to Natural Language Processing (NLP), reasoning has been recognized as a necessary underlying aspect. Many of the existing work in NLP deals with specific NLP problems in a highly heuristic manner, yet not from an...

Inferential language models for information retrieval
Jian-Yun Nie, Guihong Cao, Jing Bai
Pages: 296-322
DOI: 10.1145/1236181.1236183

Language modeling (LM) has been widely used in IR in recent years. An important operation in LM is smoothing of the document language model. However, the current smoothing techniques merely redistribute a portion of term probability according to...

Statistical query translation models for cross-language information retrieval
Jianfeng Gao, Jian-Yun Nie, Ming Zhou
Pages: 323-359
DOI: 10.1145/1236181.1236184

Query translation is an important task in cross-language information retrieval (CLIR), which aims to determine the best translation words and weights for a query. This article presents three statistical query translation models that focus on the...

A statistical framework for query translation disambiguation
Yi Liu, Rong Jin, Joyce Y. Chai
Pages: 360-387
DOI: 10.1145/1236181.1236185

Resolving ambiguity in the process of query translation is crucial to cross-language information retrieval (CLIR), given the short length of queries. This problem is even more challenging when only a bilingual dictionary is available, which is the...

Topic tracking with time granularity reasoning
Baoli Li, Wenjie Li, Qin Lu
Pages: 388-412
DOI: 10.1145/1236181.1236186

Temporal information is an important attribute of a topic, and a topic usually exists in a limited period. Therefore, many researchers have explored the utilization of temporal information in topic detection and tracking (TDT). They use either a...

Improving discriminative sequential learning by discovering important association of statistics
Xuan-Hieu Phan, Le-Minh Nguyen, Yasushi Inoguchi, Tu-Bao Ho, Susumu Horiguchi
Pages: 413-438
DOI: 10.1145/1236181.1236187

Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing or information extraction. Their key advantage is the ability to capture various...