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Jan Hajič

11 paper cards in Mova's library · 2005–2023

Jan Hajič wrote 11 of the papers Mova reads, in 2005–2023, most of them on Universal Dependencies, Treebanks, Machine translation; often with Joakim Nivre, Daniel Zeman, Filip Ginter.

OpenAlex · ORCID · h-index 39 · 8500 citations · 313 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: Universal Dependencies 5 · Treebanks 5 · Machine translation 4 · Part-of-speech tagging 2 · Dependency parsing 1 · Semantic role labeling 1

Papers

  1. Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility (2023)
    A reference paper that lists how AMR structures map onto UMR, the typologically motivated successor to AMR.
  2. UMR Annotation of Multiword Expressions (2023)
    This is a guideline paper. It runs no experiment and reports no counts. UMR, which is based on AMR, maps words to concepts. The authors define a multiword expression (MWE) operationally: several words that together form one concept. They sort MWEs into three groups. Fixed MWEs are unmodifiable word sequences, such as…
  3. Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020)
    The paper describes the UD v2 guidelines and the state of the collection at release 2.5: 157 treebanks in 90 languages, built by more than 300 contributors.
  4. CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2018)
    This overview describes the second CoNLL shared task on parsing raw text into UD. Systems start from raw text, with no gold segmentation or tags, and are scored on 82 test treebanks, with macro-averaged LAS and two new metrics, MLAS (labels plus morphological features) and BLEX (lemmas plus labels). Eight new…
  5. CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017)
    The shared task asks systems to parse raw text, with no gold tokenization, sentence splitting or tags, into UD trees for 49 languages.
  6. UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing (2016)
    UDPipe is one binary with one model per language that turns raw text into CoNLL-U: tokenisation, morphological analysis, tagging, lemmatisation and dependency parsing.
  7. Universal Dependencies v1: A Multilingual Treebank Collection (2016)
    The paper is the reference for the first UD release. It sets out the design principles: dependency-based and lexicalist annotation, the recoverability principle (every syntactic word must map back to the raw text), content words as the backbone of the tree with function words as dependents, and a closed inventory of…
  8. Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech (2014)
    The authors take 100 English sentences that already have AMR graphs, translate them into Chinese and Czech, and write AMR graphs for the translations.
  9. The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages (2009)
    This is the overview of the 2009 CoNLL shared task on joint syntactic dependency parsing and semantic role labelling.
  10. Phrase-Based and Deep Syntactic English-to-Czech Statistical Machine Translation (2008)
    The paper reports two English-to-Czech systems for WMT 2008. The first is a factored phrase-based system in Moses. Its target side is generated in two steps: word forms first, then full morphological tags, with six language models, including 7-gram models over tags. It is the stronger system, at 16.4 BLEU on the…
  11. Non-Projective Dependency Parsing using Spanning Tree Algorithms (2005)
    The paper casts weighted dependency parsing as a search for maximum spanning trees in a directed graph. Eisner's algorithm covers all projective trees in O(n^3), and the Chu-Liu-Edmonds algorithm covers non-projective trees in O(n^2), with the same edge scores. The scores are trained with online large-margin learning…

Co-authors (97)

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