International Journal of Asian Language Processing Template
Write in a clean editor, then format for International Journal of Asian Language Processing in one click — DocuGuru applies the official World Scientific template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the International Journal of Asian Language Processing format
International Journal of Asian Language Processing is a peer-reviewed journal published by World Scientific, covering Natural Language Processing Techniques, Topic Modeling, Speech Recognition and Synthesis.
| Publisher | World Scientific |
|---|---|
| Reference style | Superscript numbered (World Scientific) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. International Journal of Asian Language Processing 12, 45–58 (2023).
Formats any DOI in the closest standard style — International Journal of Asian Language Processing has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Natural Language Processing Techniques Topic Modeling Speech Recognition and Synthesis Sentiment Analysis and Opinion Mining Advanced Text Analysis Techniques |
| ISSN | 2424-791X |
| Citation impact (2-yr) | 0.97 |
| h-index | 6 |
| i10-index | 1 |
| Total citations | 139 |
| Top institutions publishing here | Beijing Language and Culture University |
| Journal website | www.colips.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in International Journal of Asian Language Processing per year
Citation impact of International Journal of Asian Language Processing by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in International Journal of Asian Language Processing
In this paper, we introduce a sentiment analysis framework and its corresponding key techniques used in AliMe, an artificial intelligent (AI) assistant for e-commerce customer service, whose fundamental ability of sentiment analysis provides support for five upper-layer application modules: user sentiment detection, user sentiment comfort, sentimental generative chatting, user service quality control and user satisfaction…
This paper details the methods proposed by the UIT-DarkCow team during their participation in the 4th Automated Legal Question Answering Competition (ALQAC 2024). Specifically, the team focused on two main tasks: legal document retrieval and legal question answering (LQA). For the legal document retrieval task, the goal was to return articles related to a given…
With the rapid development of large language models (LLMs), the quality of AI-generated context (AIGC) is rapidly improving, and the correctness and detection of generated content have become a global challenge. In this paper, we review the current methods of AIGC detector and introduce the definition, dataset and methods of AIGC detection, including the manual-based…
Automatic Image Captioning (AIC) refers to the process of synthesizing semantically and syntactically correct descriptions for images. Existing research on AIC has predominantly focused on the English language. Comparatively, lower numbers of works have focused on developing captioning systems for low-resource Indian languages like Assamese. This paper investigates AIC for the Assamese language using two…