Masatsugu Hangyo


Generating Weather Comments from Meteorological Simulations
Soichiro Murakami | Sora Tanaka | Masatsugu Hangyo | Hidetaka Kamigaito | Kotaro Funakoshi | Hiroya Takamura | Manabu Okumura
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.


Disaster Analysis using User-Generated Weather Report
Yasunobu Asakura | Masatsugu Hangyo | Mamoru Komachi
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

Information extraction from user-generated text has gained much attention with the growth of the Web.Disaster analysis using information from social media provides valuable, real-time, geolocation information for helping people caught up these in disasters. However, it is not convenient to analyze texts posted on social media because disaster keywords match any texts that contain words. For collecting posts about a disaster from social media, we need to develop a classifier to filter posts irrelevant to disasters. Moreover, because of the nature of social media, we can take advantage of posts that come with GPS information. However, a post does not always refer to an event occurring at the place where it has been posted. Therefore, we propose a new task of classifying whether a flood disaster occurred, in addition to predicting the geolocation of events from user-generated text. We report the annotation of the flood disaster corpus and develop a classifier to demonstrate the use of this corpus for disaster analysis.


Japanese Zero Reference Resolution Considering Exophora and Author/Reader Mentions
Masatsugu Hangyo | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing


Building a Diverse Document Leads Corpus Annotated with Semantic Relations
Masatsugu Hangyo | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation