Professor Manabu Okumura

Precision and Intelligence Laboratory
Advanced Information Processing Division: Okumura Group
Tokyo Institute of Technology

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NOTE: Japanese page is here.

Section: Natural Language Processing, Automated Text Summarization, Computer Assisted Language Learning, Text Data Mining

Objective: Development of the technique of natural language processing and its application systems

Current Topics:
  • Incremental Language Understanding Model(Robust Semantic and Discourse Processing)
  • Automated Text Summarization
  • Development of Communication Assistive Technology for People with Disabilities
  • Animation Control through Natural Language Understanding

  • Major

    Natural Langauge Processing, Natural Language Understanding,

    Research Interest

    Our research focuses on the development of a real-time and user-friendly speech dialogue system. We are currently working on fundamental issues for it: incremental interpretation, robustness improvement, and lexical acquisition.

    1. Incremental interpretation model for Japanese

    In a real-time dialogue system, texts need to be incrementally analyzed while they are inputed. We present such an interpretation model that uses syntactic, semantic, contextual, and commonsense knowledge cooperatively. In particular, those systems are under development which performs syntactic and semantic analysis in integrated way using case frame information of verbs, and which resolves word sense ambiguity and finds coherence between sentences simultaneously in terms of associativity between words.

    2. Ill-formed input analysis

    A user-friendly dialogue system should be robust and flexible in that it can analyze any user inputs with various constructions and unlimited vocabulary. Therefore, it needs to be able to cope with ill-formed sentences. In Japanese ellipses are typical and so for a start we present a method to fill the gaps for various types of ellipses.

    3. Semi-automatic lexical acquisition

    Large scale dictionaries are indispensable for practical natural language understanding systems. However, because it is considered to be difficult to construct a large lexicon by hand, it is an important challenge to extract lexical information semi-automatically from text data such as large corpora and machine readable dictionaries. Now those projects are in progress where information of associative relation between verbs and between nouns is extracted from a machine readable dictionary of verbs and adjectives respectively. Semi-automatic lexical acquisition from bilingual tagged corpora is also planned.



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