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Yuji Matsumoto

8 paper cards in Mova's library · 2003–2017

Yuji Matsumoto wrote 8 of the papers Mova reads, in 2003–2017, most of them on Part-of-speech tagging, Dependency parsing, Named entity recognition; often with Hiroyuki Shindo, Akihiko Kato, Frances Yung.

OpenAlex · ORCID · h-index 47 · 12438 citations · 595 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 3 · Dependency parsing 2 · Named entity recognition 1 · Discourse analysis 1 · Speech act theory 1 · Treebanks 1

Papers

  1. Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information (2017)
    A fine-grained Arabic POS tag is a conjunction of values in up to 14 morphosyntactic categories (gender, case, mood, proclitics, enclitic and others), so the number of possible tags is very large.
  2. English Multiword Expression-aware Dependency Parsing Including Named Entities (2017)
    In Penn-style treebanks, multiword expressions (MWEs) and named entities are annotated independently of the syntax, so their spans often cross the phrase structure, and dependency trees converted from them disagree with the MWE spans.
  3. Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts (2016)
    Earlier joint parsers for disfluent speech were trained and tested on human transcripts, but real input is ASR output, which adds recognition errors on top of disfluencies.
  4. Modelling the Usage of Discourse Connectives as Rational Speech Acts (2016)
    The paper predicts whether a writer marks a discourse relation with an explicit connective (but, therefore) or leaves it implicit, given the relation sense, on the Penn Discourse Treebank.
  5. Construction of an English Dependency Corpus incorporating Compound Function Words (2016)
    Penn Treebank annotation does not mark multiword expressions, and merging the nodes of a multiword unit in a word-based dependency tree can give a node with two heads or a cycle.
  6. 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…
  7. 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.
  8. Statistical Dependency Analysis with Support Vector Machines (2003)
    The paper parses Penn Treebank sentences, converted to word-word dependencies with head rules, in a deterministic bottom-up way.

Co-authors (17)

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