Kyo Kageura

The Dynamics of Terminology

The discovery of rules for the systematicity and dynamics of terminology creations is essential for a sound basis of a theory of terminology. This quest provides the driving force for The Dynamics of Terminology in which Dr. Kageura demonstrates the interaction of these two factors on a specific corpus of Japanese terminology which, beyond the necessary linguistic circumstances, also has a model character for similar studies. His detailed examination of the relationships between terms and their constituent elements, the relationships among the constituent elements and the type of conceptual combinations used in the construction of the terminology permits deep insights into the systematic thought processes underlying term creation. To compensate for the inherent limitation of a purely descriptive analysis of conceptual patterns, Dr. Kageura offers a quantitative analysis of the patterns of the growth of terminology. His fascinating and unique contribution to our understanding of the terminological process reveals the powerful interaction of linguistic possibilities and the naming process of conceptual entities.

Comparative analysis of coauthorship networks of different domains: The growth and change of networks

Many studies have tried to describe patterns of research collaboration through observing coauthorship networks. Those studies mainly analyze static networks, and most of them do not consider the development of networks. In this study, we turn our attention to the development of personal collaboration networks. On the basis of an analysis from two viewpoints, i.e., growth in the number of collaborating partners and change in the relationship strength with partners, we describe and compare the characteristics of four different domains, i.e., electrical engineering, information processing, polymer science, and biochemistry.

Brains, not brawn: The use of “smart” comparable corpora in bilingual terminology mining

Current research in text mining favors the quantity of texts over their representativeness. But for bilingual terminology mining, and for many language pairs, large comparable corpora are not available. More importantly, as terms are defined vis-à-vis a specific domain with a restricted register, it is expected that the representativeness rather than the quantity of the corpus matters more in terminology mining. Our hypothesis, therefore, is that the representativeness of the corpus is more important than the quantity and ensures the quality of the acquired terminological resources. This article tests this hypothesis on a French-Japanese bilingual term extraction task. To demonstrate how important the type of discourse is as a characteristic of the comparable corpora, we used a state-of-the-art multilingual terminology mining chain composed of two extraction programs, one in each language, and an alignment program. We evaluated the candidate translations using a reference list, and found that taking discourse type into account resulted in candidate translations of a better quality even when the corpus size was reduced by half.

Automatic thesaurus generation through multiple filtering

In this paper, we propose a method of generating bilingual keyword clusters or thesauri from parallel or comparable bilingual corpora. The method combines morphological and lexical processing, bilingual word aligmnent, and graph-theoretic cluster generation. An experiment shows that the method is promising.

A method for the comparative analysis of concentration of author productivity, giving consideration to the effect of sample size dependency of statistical measures

In this article, we propose a method for the comparative analysis of concentration in author productivity distributions. We define the notion of concentration on the basis of two viewpoints (absolute and relative concentration) and select G (Ginis index) and V (the number of authors) as suitable measures for these two viewpoints. We then discuss the statistical peculiarity of author productivity data (i.e., most of the statistical measures change systematically according to changes in the sample size) and we explain our method using developmental profiles, which takes into account the sample size dependency of statistical measures. Finally, by applying it to actual data, we demonstrate the usefulness of the proposed method.

Implicit ambiguity resolution using incremental clustering in cross-language information retrieval

This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in cross-language information retrieval (CLIR) such as Korean-to-English and Japanese-to-English CLIR. The main objective of this paper shows that document clusters can effectively resolve the ambiguities tremendously increased in translated queries as well as take into account the context of all the terms in a document. In the framework we propose, a query in Korean/Japanese is first translated into English by looking up bilingual dictionaries, then documents are retrieved for the translated query terms based on the vector space retrieval model or the probabilistic retrieval model. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is recalculated by using the clusters. In the experiment based on TREC CLIR test collection, our method achieved 39.41% and 36.79% improvement for translated queries without ambiguity resolution in Korean-to-English CLIR, and 17.89% and 30.46% improvements in Japanese-to-English CLIR, on the vector space retrieval and on the probabilistic retrieval, respectively. Our method achieved 12.30% improvement for all translation queries, compared with blind feedback for the probabilistic retrieval in Korean-to-English CLIR. These results indicate that cluster analysis help to resolve ambiguity.

A Statistical Analysis of Morphemes in Japanese Terminology

In this paper I will report the result of a quantitative analysis of the dynamics of the constituent elements of Japanese terminology. In Japanese technical terms, the linguistic contribution of morphemes greatly differ according to their types of origin. To analyse this aspect, a quantitative method is applied, which can properly characterise the dynamic nature of morphemes in terminology on the basis of a small sample.

Deverbal Compound Noun Analysis Based on Lexical Conceptual Structure

This paper proposes a principled approach for analysis of semantic relations between constituents in compound nouns based on lexical semantic structure. One of the difficulties of compound noun analysis is that the mechanisms governing the decision system of semantic relations and the representation method of semantic relations associated with lexical and contextual meaning are not obvious. The aim of our research is to clarify how lexical semantics contribute to the relations in compound nouns since such nouns are very productive and are supposed to be governed by systematic mechanisms. The results of applying our approach to the analysis of noun-deverbal compounds in Japanese and English show that lexical conceptual structure contributes to the restrictional rules in compounds.

Calculating association between technical terms based on co-occurrences in keyword lists of academic papers

In this paper, the authors evaluate a method to calculate association between specialized terms using co-occurrence information in author keywords from academic papers. When author keywords found in academic paper databases are used, co-occurrence information in units of separate compound words can easily be obtained. However, because only a few words co-occur in one paper, the problem of data sparseness arises. Thus, in this paper, the authors take into consideration indirect co-occurrence relationships when computing relatedness. They create a large-scale terminology graph which connects sets of keywords for a paper using co-occurrence relationship links, then define the distance between two sets of terms using the average path length. In addition, the authors evaluate the validity of their proposed method for calculating association by applying the terminology graph created using a real, large-scale academic paper database to the problem of automatic classification of texts, then compare the results to when direct co-occurrence and context vectors are used.

Implicit ambiguity resolution using incremental clustering in Korean-to-English cross-language information retrieval

This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in Korean-to-English cross-language information retrieval. In the framework we propose, a query in Korean is first translated into English by looking up Korean-English dictionary, then documents are retrieved based on the vector space retrieval for the translated query terms. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is re-calculated by using clusters. In experiment on TREC-6 CLIR test collection, our method achieved 28.29% performance improvement for translated queries without ambiguity resolution for queries. This corresponds to 97.27% of the monolingual performance for original queries. When we combine our method with query ambiguity resolution, our method even outperforms the monolingual retrieval.