Sadao Kurohashi

Automatic detection of discourse structure by checking surface information in sentences

In this paper, we propose an automatic method for detecting discourse structure using a variety of clues existing in the surface information of sentences. We have considered three types of clue information: clue expressions, occurrence of identical/synonymous words/phrases, and similarity between two sentences. Experimental results have shown that, in the case of scientific and technical texts, considerable part of the discourse structure can be estimated by incorporating the three types of clue information, without performing sentence understanding processes which requires giving knowledge to computers.

Finding structural correspondences from bilingual parsed corpus for corpus-based translation

In this paper, we describe a system and methods for finding structural correspondences from the paired dependency structures of a source sentence and its translation in a target language. The system we have developed finds word correspondences first, then finds phrasal correspondences based on word correspondences. We have also developed a GUI system with which a user can check and correct the correspondences retrieved by the system. These structural correspondences will be used as raw translation patterns in a corpus-based translation system.

A Fully-Lexicalized Probabilistic Model for Japanese Syntactic and Case Structure Analysis

We present an integrated probabilistic model for Japanese syntactic and case structure analysis. Syntactic and case structure are simultaneously analyzed based on wide-coverage case frames that are constructed from a huge raw corpus in an unsupervised manner. This model selects the syntactic and case structure that has the highest generative probability. We evaluate both syntactic structure and case structure. In particular, the experimental results for syntactic analysis on web sentences show that the proposed model significantly outperforms known syntactic analyzers.

Dialog Navigator: a question answering system based on large text knowledge base

This paper describes a dialog based QA system, Dialog Navigator, which can answer questions based on large text knowledge base. In real world QA systems, vagueness of questions is a big problem. Our system can navigates users to the desired answers using the following methods: asking users back with dialog cards, and description extraction of each retrieved text. Another feature of the system is that it retrieves relevant texts precisely, using question types, synonymous expression dictionary, and modifier-head relations in Japanese sentences.

TSUBAKI: An Open Search Engine Infrastructure for Developing Information Access Methodology

Due to the explosive growth in the amount of information in the last decade, it is getting extremely harder to obtain necessary information by conventional information access methods. Hence, creation of drastically new technology is needed. For developing such new technology, search engine infrastructures are required. Although the existing search engine APIs can be regarded as such infrastructures, these APIs have several restrictions such as a limit on the number of API calls. To help the development of new technology, we are running an open search engine infrastructure, TSUBAKI, on a high-performance computing environment. In this paper, we describe TSUBAKI infrastructure.

Verb Paraphrase based on Case Frame Alignment

This paper describes a method of translating a predicate-argument structure of a verb into that of an equivalent verb, which is a core component of the dictionary-based paraphrasing. Our method grasps several usages of a headword and those of the def-heads as a form of their case frames and aligns those case frames, which means the acquisition of word sense disambiguation rules and the detection of the appropriate equivalent and case marker transformation.

Fertilization of case frame dictionary for robust Japanese case analysis

This paper proposes a method of fertilizing a Japanese case frame dictionary to handle complicated expressions: double nominative sentences, non-gapping relation of relative clauses, and case change. Our method is divided into two stages. In the first stage, we parse a large corpus and construct a Japanese case frame dictionary automatically from the parse results. In the second stage, we apply case analysis to the large corpus utilizing the constructed case frame dictionary, and upgrade the case frame dictionary by incorporating newly acquired information.

Japanese case frame construction by coupling the verb and its closest case component

This paper describes a method to construct a case frame dictionary automatically from a raw corpus. The main problem is how to handle the diversity of verb usages. We collect predicate-argument examples, which are distinguished by the verb and its closest case component in order to deal with verb usages, from parsed results of a corpus. Since these couples multiply to millions of combinations, it is difficult to make a wide-coverage case frame dictionary from a small corpus like an analyzed corpus. We, however, use a raw corpus, so that this problem can be addressed. Furthermore, we cluster and merge predicate-argument examples which does not have different usages but belong to different case frames because of different closest case components. We also report on an experimental result of case structure analysis using the constructed case frame dictionary.

Automatic slide generation based on discourse structure analysis

In this paper, we describe a method of automatically generating summary slides from a text. The slides are generated by itemizing topic/non-topic parts that are extracted from the text based on syntactic/case analysis. The indentations of the items are controlled according to the discourse structure, which is detected by cue phrases, identification of word chain and similarity between two sentences. Our experiments demonstrates generated slides are far easier to read in comparison with original texts.

Chinese Dependency Parsing with Large Scale Automatically Constructed Case Structures

This paper proposes an approach using large scale case structures, which are automatically constructed from both a small tagged corpus and a large raw corpus, to improve Chinese dependency parsing. The case structure proposed in this paper has two characteristics: (1) it relaxes the predicate of a case structure to be all types of words which behaves as a head; (2) it is not categorized by semantic roles but marked by the neighboring modifiers attached to a head. Experimental results based on Penn Chinese Treebank show the proposed approach achieved 87.26% on unlabeled attachment score, which significantly outperformed the baseline parser without using case structures.