Reflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an enormous volume of information, but retrieved information is not organized. Hand-compiled encyclopedias provide organized information, but the quantity of information is limited. In this paper, aiming to integrate the advantages of both tools, we propose a method to organize a search result based on multiple viewpoints as in Wikipedia. Because viewpoints required for explanation are different depending on the type of a term, such as animal and disease, we model articles in Wikipedia to extract a viewpoint structure for each term type. To identify a set of term types, we independently use manual annotation and automatic document clustering for Wikipedia articles. We also propose an effective feature for clustering of Wikipedia articles. We experimentally show that the document clustering reduces the cost for the manual annotation while maintaining the accuracy for modeling Wikipedia articles.

Reflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an enormous volume of information, but retrieved information is not organized. Hand-compiled encyclopedias provide organized information, but the quantity of information is limited. To integrate the advantages of both tools, we have been proposing methods for encyclopedic search targeting information on the Web and patent information. In this paper, we propose a method to categorize multiple expository texts for a single term based on viewpoints. Because viewpoints required for explanation are different depending on the type of a term, such as animals and diseases, it is difficult to manually produce a large scale system. We use Wikipedia to extract a prototype of a viewpoint structure for each term type. We also use articles in Wikipedia for a machine learning method, which categorizes a given text into an appropriate viewpoint. We evaluate the effectiveness of our method experimentally.

In aiming at research and development on machine translation, we produced a test collection for Japanese-English machine translation in the seventh NTCIR Workshop. This paper describes details of our test collection. From patent documents published in Japan and the United States, we extracted patent families as a parallel corpus. A patent family is a set of patent documents for the same or related invention and these documents are usually filed to more than one country in different languages. In the parallel corpus, we aligned Japanese sentences with their counterpart English sentences. Our test collection, which includes approximately 2,000,000 sentence pairs, can be used to train and test machine translation systems. Our test collection also includes search topics for cross-lingual patent retrieval and the contribution of machine translation to a patent retrieval task can also be evaluated. Our test collection will be available to the public for research purposes after the NTCIR final meeting.

Although the World Wide Web has late become an important source to consult for the meaning of words, a number of technical terms related to high technology are not found on the Web. This paper describes a method to produce an encyclopedic dictionary for high-tech terms from patent information. We used a collection of unexamined patent applications published by the Japanese Patent Office as a source corpus. Given this collection, we extracted terms as headword candidates and retrieved applications including those headwords. Then, we extracted paragraph-style descriptions and categorized them into technical domains. We also extracted related terms for each headword. We have produced a dictionary including approximately 400,000 Japanese terms as headwords. We have also implemented an interface with which users can explore our dictionary by reading text descriptions and viewing a related-term graph.

To aid research and development in machine translation, we have produced a test collection for Japanese/English machine translation. To obtain a parallel corpus, we extracted patent documents for the same or related inventions published in Japan and the United States. Our test collection includes approximately 2000000 sentence pairs in Japanese and English, which were extracted automatically from our parallel corpus. These sentence pairs can be used to train and evaluate machine translation systems. Our test collection also includes search topics for cross-lingual patent retrieval, which can be used to evaluate the contribution of machine translation to retrieving patent documents across languages. This paper describes our test collection, methods for evaluating machine translation, and preliminary experiments.

This paper proposes a discrimination method for hierarchical relationsbetween word pairs. The method is a statistical one using an “encyclopedic corpus”' extracted and organized from Web pages. In the proposed method, we use the statistical naturethat hyponyms' descriptionstend to include hypernyms whereas hypernyms' descriptions do notinclude all of the hyponyms.Experimental results show that the method detected 61.7% of therelations in an actual thesaurus.

This paper describes the test collections produced for the Patent Retrieval Task in the Fifth NTCIR Workshop. We performed the invalidity search task, in which each participant group searches a patent collection for the patents that can invalidate the demand in an existing claim. For this purpose, we performed both document and passage retrieval tasks. We also performed the automatic patent classification task using the F-term classification system. The test collections will be available to the public for research purposes.

In response to growing needs for cross-lingual patent retrieval, we propose PRIME (Patent Retrieval In Multilingual Environment system), in which users can retrieve and browse patents in foreign languages only by their native language. PRIME translates a query in the user language into the target language, retrieves patents relevant to the query, and translates retrieved patents into the user language. To update a translation dictionary, PRIME automatically extracts new translations from parallel patent corpora. In the current implementation, trilingual (J/E/K) patent retrieval is available. We describe the system design and its evaluation.

Cross-language information retrieval (CLIR), where queries and documents are in different languages, needs a translation of queries and/or documents, so as to standardize both of them into a common representation. For this purpose, the use of machine translation is an effective approach. However, computational cost is prohibitive in translating large-scale document collections. To resolve this problem, we propose a two-stage CLIR method. First, we translate a given query into the document language, and retrieve a limited number of foreign documents. Second, we machine translate only those documents into the user language, and re-rank them based on the translation result. We also show the effectiveness of our method by way of experiments using Japanese queries and English technical documents.