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Keisuke Sakaguchi

5 paper cards in Mova's library · 2012–2020

Keisuke Sakaguchi wrote 5 of the papers Mova reads, in 2012–2020, most of them on Part-of-speech tagging, Universal Dependencies, Semantic role labeling; often with Chandra Bhagavatula, Ronan Le Bras, Yejin Choi.

OpenAlex · ORCID · h-index 27 · 3103 citations · 110 works — openalex, 2026-10-18

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 · ORCID

Topics: Part-of-speech tagging 2 · Universal Dependencies 1 · Semantic role labeling 1

Ideas Mova took (1)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

Papers

  1. COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs (2020) measured
    The paper argues that hand-built commonsense knowledge graphs can never cover everything an NLP system will meet.
  2. Abductive Commonsense Reasoning (2019)
    The paper introduces abductive reasoning over narratives. Given two observations O1 and O2, a system must pick the more plausible explanation from two hypotheses (αNLI), or generate one (αNLG). The ART dataset has about 20k contexts taken from ROCStories and over 200k hypotheses, written by crowdworkers and then…
  3. Universal Decompositional Semantics on Universal Dependencies (2016) 1 idea kept
    The Decomp project adds semantic annotation on top of UD treebanks by asking ordinary speakers simple, decomposed questions with Likert-scale answers instead of asking for labels like Agent.
  4. Construction of English MWE Dictionary and its Application to POS Tagging (2013)
    The authors build an English multiword-expression (MWE) dictionary from the English Wiktionary. They take fixed functional expressions (adverbs, prepositions, conjunctions, determiners, prepositional phrases and pronouns, such as "after all", "according to", "no one") and leave out compound nouns, phrasal verbs and…
  5. Joint English Spelling Error Correction and POS Tagging for Language Learners Writing (2012)
    Spelling correction is usually a pipeline step before tagging, but it cannot fix learner errors that produce real words (homophones, confusions, split and merge errors, inflection and derivation errors such as "badly" for "bad"), and the tagger needs the context to choose the fix.

Co-authors (28)

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