Hokkaido University · Computer Science
Professor Masaharu Yoshioka's research lab specializes in information retrieval, natural language processing, and intelligent document analysis, with a focus on enhancing the accuracy and usability of legal and news text understanding systems. The lab develops advanced NLP techniques such as BERT-based models, query reformulation methods, and data augmentation strategies to address challenges in low-resource and complex text environments. Key research directions include legal textual entailment, automatic summarization of multi-document news, and the formalization of design and information retrieval knowledge for intelligent systems. The lab emphasizes practical applications in legal technology, news summarization, and knowledge-intensive information systems.
Figures are computed from collected data and may differ slightly.
The Competition on Legal Information Extraction/Entailment (COLIEE) statute law legal textual entailment task (task 4) is a task to make a system judge whether a given question statement is true or not by provided articles. In the last COLIEE 2020, the best performance system used bidirectional encoder representations from transformers (BERT), a deep-learning-based natural language processing tool for handling word semantics by considering their context. However, there are problems related to th
In this paper, we describe our method to generate summary from multiple news articles. Since most of news articles report several events and these events are refereed with following articles, we use this event reference information to calculate importance of a sentence in multiple news articles. We also propose a method to delete redundant description by using similarity of events. Finally we discuss its effectiveness based on the evaluation result.
Even though a Boolean query can express the information need precisely enough to select relevant documents, it is not easy to construct an appropriate Boolean query that covers all relevant documents. To utilize a Boolean query effectively, a mechanism to retrieve as many as possible relevant documents is therefore required. In accordance with this requirement, we propose a method for modifying a given Boolean query by using information from a relevant document set. The retrieval results, howeve
Abstract An intelligent CAD system is not merely a set of intelligent design tools, but rather it must be an intelligent, integrated design environment. This requests it must be equipped with a large scale knowledge base in which design knowledge is intensively and systematically stored. To do so, design knowledge must be systematically formalized, made computable, and organized. The present paper investigates fundamental issues of systematization of design knowledge. Design knowledge has two ca
When implementing an IR system that can support comprehensive searches from a wide variety of documents, it is crucial to have a mechanism for selecting appropriate query formulations. For this purpose, many IR systems can modify query terms by estimating the user's information need. However, because modified query terms are usually represented in a complicated form, it is difficult to judge how appropriate they are. In this research, we assume all relevant documents should contain words that co
Even though a Boolean query can express the information need precisely enough to select relevant documents, it is not easy to construct an appropriate Boolean query that covers all relevant documents. To utilize a Boolean query effectively, a mechanism to retrieve as many as possible relevant documents is therefore required. In accordance with this requirement, we propose a method for modifying a given Boolean query by using information from a relevant document set. The retrieval results, howeve
Abstract Most of the previous research efforts for design process modeling had such assumptions as “design as problem solving,” “design as decision making,” and “design by analysis,” and did not explicitly address “design as synthesis.” These views lack notion and understanding about synthesis. Compared with analysis, synthesis is less understood and clarified. This paper discusses our fundamental view on synthesis and approach toward a reasoning framework of design as synthesis. To do so, we ob
Open papers in the app to read, cite, and organize with AI.