Koji Matsuda


Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities
Satoshi Sekine | Kouta Nakayama | Masako Nomoto | Maya Ando | Asuka Sumida | Koji Matsuda
Proceedings of the 29th International Conference on Computational Linguistics

This paper describes a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE), which has 219 fine-grained NE categories. We first categorized 920 K Japanese Wikipedia pages according to the ENE scheme using machine learning, followed by manual validation. We then organized a shared task of Wikipedia categorization into 30 languages. The training data were provided by Japanese categorization and the language links, and the task was to categorize the Wikipedia pages into 30 languages, with no language links from Japanese Wikipedia (20M pages in total). Thirteen groups with 24 systems participated in the 2020 and 2021 tasks, sharing their outputs for resource-building. The Japanese categorization accuracy was 98.5%, and the best performance among the 30 languages ranges from 80 to 93 in F-measure. Using ensemble learning, we created outputs with an average F-measure of 86.8, which is 1.7 better than the best single systems. The total size of the resource is 32.5M pages, including the training data. We call this resource creation scheme “Resource by Collaborative Contribution (RbCC)”. We also constructed structuring tasks (attribute extraction and link prediction) using RbCC under our ongoing project, “SHINRA.”


Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension
Diana Galvan-Sosa | Jun Suzuki | Kyosuke Nishida | Koji Matsuda | Kentaro Inui
Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.


Investigating the Challenges of Temporal Relation Extraction from Clinical Text
Diana Galvan | Naoaki Okazaki | Koji Matsuda | Kentaro Inui
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain. The complexity of temporal representation in language is evident as results of the 2016 Clinical TempEval challenge indicate: the current state-of-the-art systems perform well in solving mention-identification tasks of event and time expressions but poorly in temporal relation extraction, showing a gap of around 0.25 point below human performance. We explore to adapt the tree-based LSTM-RNN model proposed by Miwa and Bansal (2016) to temporal relation extraction from clinical text, obtaining a five point improvement over the best 2016 Clinical TempEval system and two points over the state-of-the-art. We deliver a deep analysis of the results and discuss the next step towards human-like temporal reasoning.


Neural Joint Learning for Classifying Wikipedia Articles into Fine-grained Named Entity Types
Masatoshi Suzuki | Koji Matsuda | Satoshi Sekine | Naoaki Okazaki | Kentaro Inui
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters

Building a Corpus for Japanese Wikification with Fine-Grained Entity Classes
Davaajav Jargalsaikhan | Naoaki Okazaki | Koji Matsuda | Kentaro Inui
Proceedings of the ACL 2016 Student Research Workshop


Annotating Geographical Entities on Microblog Text
Koji Matsuda | Akira Sasaki | Naoaki Okazaki | Kentaro Inui
Proceedings of the 9th Linguistic Annotation Workshop