Papers by Yuki Yamamoto
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation (2024.findings-eacl)
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Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
| Challenge: | et al., 2006) considers geographic relatedness among geo-entity mentions in document-level geoparsing. |
| Approach: | They present a Japanese travelogue dataset that considers geographic relatedness among geo-entity mentions. |
| Outcome: | The proposed dataset includes 200 travelogue documents with rich geo-entity information . it shows that human activities, mobility, and events are often described with natural language expressions of locations or geographic entities (geo-entities) |
Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)
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Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
| Challenge: | Knowledge graphs (KGs) are incomplete and miss some information. |
| Approach: | They propose to learn entity representations via a graph structure that uses Seen-entities, Unseen-Entities and words as nodes created from the descriptions of all entities. |
| Outcome: | The proposed method improves relation prediction for the entity pairs containing Unseen-entities. |
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing (2021.starsem-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure. |
| Approach: | They propose to use a dictionary to capture the structure of complex sentences . they train models on data derived from AMR and Wikipedia corpus . |
| Outcome: | The proposed model will be made public and the proposed patterns will be validated. |