Chaveevan Pechsiri


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Building an Annotated Corpus for Text Summarization and Question Answering
Patcharee Varasai | Chaveevan Pechsiri | Thana Sukvari | Vee Satayamas | Asanee Kawtrakul
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We describe ongoing work in semi-automatic annotating corpus, with the goal to answer why-question in question answering system and give a construction of the coherent tree for text summarization. In this paper we present annotation schemas for identifying the discourse relations that hold between the parts of text as well as the particular textual of span that are related via the discourse relation. Furthermore, we address several tasks in building the annotated corpus in discourse level, namely creating annotated guidelines, ensuring annotation accuracy and evaluating.

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Know-Why Extraction from Textual Data for Supporting What Questions
Chaveevan Pechsiri | Phunthara Sroison | J. Janviriyasopak
Coling 2008: Proceedings of the workshop on Knowledge and Reasoning for Answering Questions


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Ontology Driven K-Portal Construction and K-Service Provision
Asanee Kawtrakul | Chaveevan Pechsiri | Trakul Permpool | Dussadee Thamvijit | Phukao Sornprasert | Chaiyakorn Yingsaeree | Mukda Suktarachan
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Knowledge has been crucial for the country’s development and business intelligence, where valuable knowledge is distributed over several websites with heterogeneous formats. Moreover, finding the needed information is a complex task since there has been lack of semantic relation and organization. Even if it has been found, an overload may occur because there is no content digestion. This paper focuses on ontology-driven knowledge extraction with natural language processing techniques and a framework of usercentric design for accessing the required information based on their demands. These demands can be expressed in the form of Knowwhat, Know-why, Know-where, Know-when, Know-how, and Know-who for a question answering system.