Makoto Iwayama

Cluster-based text categorization: a comparison of category search strategies

Text categorization can be viewed asaprocessof catego~ search, in which one or more categories for a testdocument are searchedfor by using given training documents with known categories. In this paper a cluster-based search with a probabilistic clustering algorithm is proposed and evaluated on two data sets. The “efficiency, effectiveness, and noise tolerance of this search strategy were confirmed to be better than those of a full search, a category-based search, and a cluster-based search with nonprobabilistic clustering.

Overview of Patent Retrieval Task at NTCIR-3

We describe the overview of patent retrieval task at NTCIR-3. The main task was the technical survey task, where participants tried to retrieve relevant patents to news articles. In this paper, we introduce the task design, the patent collections, the characteristics of the submitted systems, and the results overview. We also arranged the free-styled task, where participants could try anything they want as far as the patent collections were used. We describe the brief summaries of the proposals submitted to the free-styled task.

Patent Claim Processing for Readability - Structure Analysis and Term Explanation

Patent corpus processing should be centered around patent claim processing because claims are the most important part in patent specifications. It is common that claims written in Japanese are described in one sentence with peculiar style and wording and are difficult to understand for ordinary people. The peculiarity is caused by structural complexity of the sentences and many difficult terms used in the description. We have already proposed a framework to represent the structure of patent claims and a method to automatically analyze it. We are currently investigating a method to clarify terms in patent claims and to find the explanatory portions from the detailed description part of the patent specifications. Through both approaches, we believe we can improve readability of patent claims.

Relevance feedback with a small number of relevance judgements: incremental relevance feedback vs. document clustering

The use of incremental relevance feedback and document clustering were investigated in an relevance feedback environment in which the number of relevance judgements was quite small. Through experiments on the TREC collection, the incremental relevance feedback approach was found not to improve the overall search effectiveness. The clustering approach was found to be promising, although it sometimes over-focuses on a particular topic in a query and ignores the others. To overcome this problem, a query-biased clustering algorithm was developed and shown to be effective.

Introduction to the special issue on patent processing

The processing of intellectual property documents, such as patents, has been important to the industry, business, and law communities. Recently, the importance of patent processing has also been recognized in academic research communities, particularly by information retrieval and natural language processing researchers. In addition, large test collections that include patents have recently become available, to enable the systematic evaluation of methodologies from a scientific point of view. In the light of these activities, this special issue is intended to collect advanced research papers on patent processing. As an introduction to the special issue on patent processing, this paper surveys the relevant literature and outlines the papers selected for the special issue.

Proposal of two-stage patent retrieval method considering the claim structure

The importance of patents is increasing in global society. In preparing a patent application, it is essential to search for related patents that may invalidate the invention. However, it is time-consuming to identify them among the millions of patents. This article proposes a patent-retrieval method that considers a claim structure for a more accurate search for invalidity. This method uses a claim text as input; it consists of two retrieval stages. In stage 1, general text analysis and retrieval methods are applied to improve recall. In stage 2, the top N documents retrieved in stage 1 are rearranged to improve precision by applying text analysis and retrieval methods using the claim structure. Our two-stage retrieval introduces five precision-oriented analysis and retrieval methods: query-term extraction from a portion of a claim text that describes the characteristics of a claim; query term-weighting without term frequency; query term-weighting with “measurement terms”; text retrieval using only claims as a target; and calculating the relevant score by “partially” adding scores in stage 2 to those in stage 1. Evaluation results using test sets of the NTCIR4 Patent Retrieval Task show that our methods are effective, though the degree of the effectiveness varies depending on the test sets.

An empirical study on retrieval models for different document genres: patents and newspaper articles

Reflecting the rapid growth in the utilization of large test collections for information retrieval since the 1990s, extensive comparative experiments have been performed to explore the effectiveness of various retrieval models. However, most collections were intended for retrieving newspaper articles and technical abstracts. In this paper, we describe the process of producing a test collection for patent retrieval, the NTCIR-3 Patent Retrieval Collection, which includes two years of Japanese patent applications and 31 topics produced by professional patent searchers. We also report experimental results obtained by using this collection to re-examine the effectiveness of existing retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and newspaper articles. Issues related to patent retrieval are also discussed.

Evaluating patent retrieval in the third NTCIR workshop

Reflecting the rapid growth in the utilization of large test collections for information retrieval since the 1990s, extensive comparative experiments have been performed to explore the effectiveness of various retrieval models. However, most collections were intended for retrieving newspaper articles and technical abstracts. In this paper, we describe the process of producing a test collection for patent retrieval, the NTCIR-3 Patent Retrieval Collection, which includes two years of Japanese patent applications and 31 topics produced by professional patent searchers. We also report experimental results obtained by using this collection to re-examine the effectiveness of existing retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and newspaper articles. Issues related to patent retrieval are also discussed.

Information Access Based on Associative Calculation

The statistical measures for similarity have been widely used in textual information retrieval for many decades. They are the basis to improve the effectiveness of IR systems, including retrieval, clustering, and summarization. We have developed an information retrieval system DualNAVI which provides users with rich interaction both in document space and in word space. We show that associative calculation for measuring similarity among documents or words is the computational basis of this effective information access with DualNAVI. The new approaches in document clustering (Hierarchical Bayesian Clustering), and measuring term representativeness (Baseline method) are also discussed. Both have sound mathematical basis and depend essentially on associative calculation.

The patent retrieval task in the fourth NTCIR workshop

This paper describes the Patent Retrieval Task in the Fourth NTCIR Workshop, and the test collections produced in this task. We perform 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. We also perform the automatic patent map generation task, in which the patents associated with a specific topic are organized in a multi-dimensional matrix.