ACM Transactions on Asian Language Information Processing

Aims & Scope

The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers, survey papers, and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines.

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Arabic Natural Language Processing

  Citation: 309

  Authors: Ali, Khaled

• An ontology of time for the semantic web

  Citation: 218

  Authors: Jerry R., Feng

• An evaluation of statistical spam filtering techniques

  Citation: 193

  Authors: Le, Jingbo, Tianshun

• Emotion recognition from text using semantic labels and separable mixture models

  Citation: 155

  Authors: Chung-Hsien, Ze-Jing, Yu-Chung

• Stemming Indonesian

  Citation: 97

  Authors: Mirna, Jelita, Bobby, S. M.M., Hugh E.

• Handwriting Recognition in Indian Regional Scripts

  Citation: 84

  Authors: Umapada, Ramachandran, Nabin

• An adaptive <i>k</i> -nearest neighbor text categorization strategy

  Citation: 77

  Authors: Li, Lu, Yu

• Rapid development of Hindi named entity recognition using conditional random fields and feature induction

  Citation: 67

  Authors: Wei, Andrew

• Flexible pseudo-relevance feedback via selective sampling

  Citation: 58

  Authors: Tetsuya, Toshihiko, Makoto

• Toward a unified approach to statistical language modeling for Chinese

  Citation: 55

  Authors: Jianfeng, Joshua, Mingjing, Kai-Fu