Takehito Utsuro

STRUCTURAL MATCHING OF PARALLEL TEXTS

This paper describes a method for finding structural matching between parallel sentences of two languages, (such as Japanese and English). Parallel sentences are analyzed based on unification grammars, and structural matching is performed by making use of a similarity measure of word pairs in the two languages. Syntactic ambiguities are resolved simultaneously in the matching process. The results serve as a useful source for extracting linguistic and lexical knowledge.

Bilingual text, matching using bilingual dictionary and statistics

This paper describes a unified framework for bilingual text matching by combining existing hand-written bilingual dictionaries and statistical techniques. The process of bilingual text matching consists of two major steps: sentence alignment and structural matching of bilingual sentences. Statistical techniques are applied to estimate word correspondences not included in bilingual dictionaries. Estimated word correspondences are useful for improving both sentence alignment and structural matching.

Galatea: Open-Source Software for Developing Anthropomorphic Spoken Dialog Agents

Galatea is a software toolkit to develop a human-like spoken dialog agent. In order to easily integrate the modules of different characteristics including speech recognizer, speech synthesizer, facial animation synthesizer, and dialog controller, each module is modeled as a virtual machine having a simple common interface and connected to each other through a broker (communication manager). Galatea employs model-based speech and facial animation synthesizers whose model parameters are adapted easily to those for an existing person if his or her training data is given. The software toolkit that runs on both UNIX/Linux and Windows operating systems will be publicly available in the middle of 2003 [7, 6].

Named entity chunking techniques in supervised learning for Japanese named entity recognition

This paper focuses on the issue of named entity chunking in Japanese named entity recognition. We apply the supervised decision list learning method to Japanese named entity recognition. We also investigate and incorporate several named-entity noun phrase chunking techniques and experimentally evaluate and compare their performance. In addition, we propose a method for incorporating richer contextual information as well as patterns of constituent morphemes within a named entity, which have not been considered in previous research, and show that the proposed method outperforms these previous approaches.

Thesaurus-based efficient example retrieval by generating retrieval queries from similarities

In example-based NLP, the problem of computational cost of example retrieval is severe, since the retrieval time increases in proportion to the number of examples in the database. This paper proposes a novel example retrieval method for avoiding full retrieval of examples. The proposed method has the following three features, 1) it generates retrieval queries from similarities, 2) efficient example retrieval through the tree structure of a thesaurus, 3) binary search along subsumption ordering of retrieval queries. Example retrieval time drastically decreases with the method.

Lexical knowledge acquisition from bilingual corpora

For practical research in natural language processing, it is indispensable to develop a large scale semantic dictionary for computers. It is especially important to improve the techniques for compiling semantic dictionaries from natural language texts such as those in existing human dictionaries or in large corpora. However, there are at least two difficulties in analyzing existing texts: the problem of syntactic ambiguities and the problem of polysemy. Our approach to solve these difficulties is to make use of translation examples in two distinct languages that have quite different syntactic structures and word meanings. The reason we took this approach is that in many cases both syntactic and semantic ambiguities are resolved by comparing analyzed results from both languages. In this paper, we propose a method for resolving the syntactic ambiguities of translation examples of bilingual corpora and a method for acquiring lexical knowledge, such as case frames of verbs and attribute sets of nouns.

Compilation of a dictionary of japanese functional expressions with hierarchical organization

The Japanese language has a lot of functional expressions, which consist of more than one word and behave like a single functional word. A remarkable characteristic of Japanese functional expressions is that each functional expression has many different surface forms. This paper proposes a methodology for compilation of a dictionary of Japanese functional expressions with hierarchical organization. We use a hierarchy with nine abstraction levels: the root node is a dummy node that governs all entries; a node in the first level is a headword in the dictionary; a leaf node corresponds to a surface form of a functional expression. Two or more lists of functional expressions can be integrated into this hierarchy. This hierarchy also provides a way of systematic generation of all different surface forms. We have compiled the dictionary with 292 headwords and 13,958 surface forms, which covers almost all of major functional expressions.

Applying a Burst Model to Detect Bursty Topics in a Topic Model

This paper focuses on two types of modeling of information flow in news stream, namely, burst analysis and topic modeling. First, when one wants to detect a kind of topics that are paid much more attention than usual, it is usually necessary for him/her to carefully watch every article in news stream at every moment. In such a situation, it is well known in the field of time series analysis that Kleinberg’s modeling of bursts is quite effective in detecting burst of keywords. Second, topic models such as LDA (latent Dirichlet allocation) are also quite effective in estimating distribution of topics over a document collection such as articles in news stream. However, Kleinberg’s modeling of bursts is usually applied only to bursts of keywords but not to those of topics. Considering this fact, we propose how to apply Kleinberg’s modeling of bursts to topics estimated by a topic model such as LDA and DTM (dynamic topic model).

Evaluating effects of machine translation accuracy on cross-lingual patent retrieval

We organized a machine translation (MT) task at the Seventh NTCIR Workshop. Participating groups were requested to machine translate sentences in patent documents and also search topics for retrieving patent documents across languages. We analyzed the relationship between the accuracy of MT and its effects on the retrieval accuracy.

Analysing features of Japanese splogs and characteristics of keywords

This paper focuses on analyzing (Japanese) splogs based on various characteristics of keywords contained in them. We estimate the behavior of spammers when creating splogs from other sources by analyzing the characteristics of keywords contained in splogs. Since splogs often cause noises in word occurrence statistics in the blogosphere, we assume that we can efficiently (manually) collect splogs by sampling blog homepages containing keywords of a certain type on the date with its most frequent occurrence. We manually examine various features of collected blog homepages regarding whether their text content is excerpt from other sources or not, as well as whether they display affiliate advertisement or out-going links to affiliated sites. Among various informative results, it is important to note that more than half of the collected splogs are created by a very small number of spammers.