What's New

We will present following papers at EMNLP2016 (2016/11/1-5) (7/31)

  • Naoki Otani, Toshiaki Nakazawa, Daisuke Kawahara and Sadao Kurohashi: IRT-based Aggregation Model of Crowdsourced Pairwise Comparison for Evaluating Machine Translations
  • Toshiaki Nakazawa and Sadao Kurohashi: Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation

Dr. Morita was awarded a best paper award of IPSJ national convention in the 78th national convention of IPSJ with the following paper. (7/29)

  • Development of Practical Japanese Morphological Analysis using Recurrent Neural Network Language Model

Prof. Kurohashi was appointed to the reserach supervisor of PRESTO "Fundamental Information Technologies toward Innovative Social System Design." (6/5)

We will present following papers at ACL2016 (2016/8/7-12) (5/25)

  • Tomohide Shibata, Daisuke Kawahara and Sadao Kurohashi: Neural Network-Based Model for Japanese Predicate Argument Structure Analysis
  • Hitoshi Otsuki, Chenhui Chu, Toshiaki Nakazawa and Sadao Kurohashi: Dependency Forest based Word Alignment (Student Research Workshop)
  • Raj Dabre, Yevgeniy Puzikov, Fabien Cromieres and Sadao Kurohashi: The Kyoto University Cross-Lingual Pronoun Translation System (WMT 2016)
  • Yu Shen, Chenhui Chu, Fabien Cromieres and Sadao Kurohashi: Cross-language Projection of Dependency Trees with Constrained Partial Parsing for Tree-to-Tree Machine Translation (WMT 2016)

Research Overview

Language is the most reliable medium of human intellectual activities. Our objective is to establish the technology and academic discipline for handling and understanding language, in a manner that is as close as possible to that of humans, using computers. These include syntactic language analysis, semantic analysis, context analysis, text comprehension, text generation and dictionary systems to develop various application systems for machine translation and information retrieval.

Search Engine Infrastructure based on Deep Natural Language Processing


The essential purpose of information retrieval is not to retrieve just a relevant document but to acquire the information or knowledge in the document. We have been developing a next-generation infrastructure of information retrieval on the basis of the following techniques of deep natural language processing: precise processing based not on words but on predicate-argument structures, identifying the variety of linguistic expressions and providing a bird's-eye view of search results via clustering and interaction.

Machine Translation


To bring automatic translation by computers to the level of human translation, we have been studying next-generation methodology of machine translation on the basis of text understanding and a large collection of translation examples. We have already accomplished practical translation on the domain of travel conversation, and constructed a translation-aid system that can be used by experts of patent translation.

Fundamental Studies on Text Understanding

To make computers understand language, it is essential to give computers world knowledge. This was a very hard problem ten years ago, but it has become possible to acquire knowledge from a massive amount of text in virtue of the drastic progress of computing power and network. We have successfully acquired linguistic patterns of predicate-argument structures from automatic parses of 7 billion Japanese sentences crawled from the Web using grid computing machines. By utilizing such knowledge, we study text understanding, i.e., recognizing the relationships between words and phrases in text.