Prof. Dr. Virach Sornlertlamvanich was esteemed "The Researcher of the Year 2001" by the Nation Newspaper (Thailand). Observing his continuous contributions in the field of Computer Engineering, he was conclusively awarded "National Distinguished Researcher Award 2003" in Information Technology and Communication by the National Research Council of Thailand, "ASEAN Outstanding Engineering Achievement Award 2011" by ASEAN Federation of Engineering Organizations (AFEO), and followed by “Outstanding Alumni Award, Tokyo Tech Alumni Association (Thailand Chapter)” in 2021.
He received a doctoral degree in Computer Engineering from the Tokyo Institute of Technology in 1998. He worked with NEC Corporation as a sub-project leader for Thai language processing in the Multi-lingual Machine Translation Project. He joined the National Electronics and Computer Technology Center (NECTEC) in 1992. His research interests are in the area of Natural Language Processing, Machine Translation, Information Retrieval, Knowledge Engineering, and Artificial Intelligence. His recent efforts are on the research and development of technology for Digitized Thailand 2009 which is aimed to establish a service platform for digital content and applications to accomplish the creative industry. He is also a pioneer in establishing an AI research platform for Thammasat AI City 2020 at Rangsit Campus during 2020-2023.
Social Media Understanding
Text from social media is significant key information to understand social movement. However, the length of the social media text is typically short and concise with a lot of absent words. Our task is to identify the proper keyword representing the message content that we are accounting for. Instead of training the model for keyword extraction directly from the Twitter messages, we propose a new method to fine-tune the model trained from some known documents containing richer context information. We conducted the experiment on Twitter messages and expressed in word cloud timeline. It shows a promising result.
Asian WordNet (http://www.asianwordnet.org)
WordNet is widely used in NLP research because its important feature of computability. Each word in WordNet is expressed in a set of synonym words called synset, and defined in a semantic relational structure to each other. The based WordNet is created for the English language definition. Since the words in WordNet are defined by set of words, it is quite acceptable to generate other language WordNet by translating each word in the synset. We proposed an algorithm that can disambiguate the word by considering the synonym, and its English translation. Asian WordNet (AWN) is therefore generated for many Asian languages by using each local English dictionary.
The aim of the project is to create the country digital platform. We realize that many database and applications cannot be easily shared or connected to provide a higher integrated solution. Initially, the project stimulated the data digitization and application development following the provided API standard. Beyond the efforts in Digitized Thailand initiative, we created a huge useful database such as cultural information, language corpora, and in the same time many research algorithms such as word segmentation, keyword extraction, information extraction have also been developed to provide a service via the standard API.
In current statistical approach for NLP research, collections of language resources are crucial in generating the language model. Many types of language resources can be prepared depending on the purpose of study. They can be part-of-speech (POS) tagged corpus, bracketed corpus (syntactic and/or semantic annotated corpus, or parse tree corpus), parallel translated corpus, speech corpus and several types of lexicon. To generate such a kind of language resource there are many issues to overcome such as annotation consistency, word/phrase level alignment, multi level annotation, standardization since we have to handle a large number of language data.
1995 - 1998
Computer Engineering, Ph.D.
Tokyo Insitute of Technology, Japan
1984 - 1986
Precision Mechanics, M.Eng.
Kyoto University, Japan
1980 - 1984
Precision Mechanics, B.Eng.
Kyoto University, Japan
Thammasat AI City
Thammasat AI City initiative has an aim to establish a resilient AI platform for the inhabitants to find out the opportunities and challenges of AI disruption. Rangsit campus is where Thammasat University is located, surrounded by research and higher education facilities, industrial, business, and agricultural areas. To confront the AI disruption, Rangsit campus is geared to be a role model of AI City for the full activation of the use of data and physical availability. The Thammasat AI City focuses on the four domains of elderly and healthcare, mobility, agriculture, and environment under the awareness of societal change after the COVID-19 pandemic technology trends namely distributed city, human traceability, new reality, home-office integration, contactless technology, digital lending, and frugal innovation4.