Noriki Nishida
Noriki Nishida has 4 papers in our library, in 2022–2026, most of them on Discourse analysis, Dependency parsing, Question answering; often with Yuji Matsumoto, Fei Cheng, Yuki Arase.
OpenAlex · ORCID · h-index 7 · 41 works · 189 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 22 times in our library · ⭐ Paper Stars ranking: #8415
Affiliations
Fields, topics and concepts
📚 Shelves 12The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksBiochemistry, Genetics and Molecular Biology · 1 workComputational Theory and Mathematics · 1 workComputer Vision and Pattern Recognition · 1 workDevelopmental and Educational Psychology · 1 workHuman-Computer Interaction · 1 workInformation Systems · 1 workMolecular Biology · 1 workPsychology · 1 work
🧭 Topics 3Research subjects and languages the work is about.Dependency parsing · 1 workDiscourse analysis · 1 workQuestion answering · 1 work
Works by year
2020s
Papers
- Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022 · 31 citations)
Discourse dependency parsers trained on one annotated treebank lose much of their accuracy on text from a different domain, and labeled data for new domains is scarce and expensive. - Applicability Condition Extraction for Therapeutic Drug-Disease Relations (2026)
Most biomedical relation extraction work only asks whether a drug treats a disease. It ignores the circumstances that make the treatment work, such as dose, patient group or coexisting illness. Luo et al. define a new task: given a drug–disease pair from a PubMed abstract, find the text spans that state these… - Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering (2026)
GraphRAG systems build a knowledge graph from source texts and retrieve verbalized parts of it to ground LLM answers. However, it has been unclear which pipeline components actually drive performance, especially for factoid and multi-hop questions rather than corpus summarization. - J-Shuwa: A Large-Scale Web-Collected Japanese Sign Language-Japanese Parallel Corpus (2026)
Japanese Sign Language (JSL) has seen little AI research, mainly because no large public parallel corpora exist.
Co-authors here
Venues
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
- Transactions of the Association for Computational Linguistics (TACL) ×2: Transactions of the Association for Computational Linguistics (TACL) 2026, Transactions of the Association for Computational Linguistics (TACL) 2022
Cited by
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