ACM Transactions on Asian and Low-Resource Language Information Processing

Aims & Scope

The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival 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.

The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP.

Emphasis will be placed on the originality and the practical significance of the reported research.

View Aims & Scope

---

Metrics & Ranking

Impact Factor

Year	Value
2025	2
2024	1.80

Journal Rank

Year	Value
2024	11523

Journal Citation Indicator

Year	Value
2024	1507

SJR (SCImago Journal Rank)

Year	Value
2024	0.503

Quartile

Year	Value
2024	Q2

---

---

Abstracting & Indexing

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

• Scopus
• Google Scholar
• Web of Science

---

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.

• A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models

  Citation: 107

  Authors: Usman, Imran, Shah Khalid, Mukesh

• A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents

  Citation: 94

  Authors: Praphula Kumar, Vijayalakshmi, Rajendra

• Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model

  Citation: 71

  Authors: Vankayala, Korra, Bibhudatta

• A Survey of Opinion Mining in Arabic

  Citation: 57

  Authors: Gilbert, Ramy, Hazem, Wassim, Khaled Bashir, Nizar, Ahmad, Ali

• Hate Speech Detection in Roman Urdu

  Citation: 56

  Authors: Muhammad Moin, Khurram, Muhammad Kamran

• Context-aware Emotion Detection from Low-resource Urdu Language Using Deep Neural Network

  Citation: 54

  Authors: Muhammad Farrukh, Abdul Rehman, Muhammad Umair, Thippa Reddy, Waseem, Mirza Omer

• AROMA

  Citation: 53

  Authors: Ahmad, Ramy, Hazem, Khaled Bashir, Wassim, Gilbert

• Isolated Arabic Sign Language Recognition Using a Transformer-based Model and Landmark Keypoints

  Citation: 49

  Authors: Sarah, Hamzah, Mohammad

• A Sentiment Treebank and Morphologically Enriched Recursive Deep Models for Effective Sentiment Analysis in Arabic

  Citation: 45

  Authors: Ramy, Hazem, Nizar, Khaled Bashir, Wassim

• Emoji-Based Sentiment Analysis Using Attention Networks

  Citation: 43

  Authors: Yinxia, Yue, Fei, Tao, Donghong

---