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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.

4 papers in our library · 2022–2026

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

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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…
  3. 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.
  4. 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

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 22 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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