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Josef van Genabith

7 paper cards in Mova's library · 1998–2011

Josef van Genabith wrote 7 of the papers Mova reads, in 1998–2011, most of them on Machine translation; often with Jennifer Foster, Joachim Wagner, Lamia Tounsi.

OpenAlex · ORCID · h-index 38 · 5517 citations · 366 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: Machine translation 1

Papers

  1. An Open-Source Finite State Morphological Transducer for Modern Standard Arabic (2011)
    Attia et al. build AraComLex, an open-source finite-state morphological analyzer for Modern Standard Arabic, compiled with Foma from a lemma-based lexc lexicon and about 130 alteration rules. They grow a seed lexicon of 10,799 lemmas to 30,587 lemmas. The additions come from a billion-word corpus pre-annotated with…
  2. Morphological Features for Parsing Morphologically-rich Languages: A Case of Arabic (2011)
    Dehdari, Tounsi and van Genabith ask which morphological features, folded into the POS tag, help a PCFG latent-annotation parser (a re-implemented Berkeley parser) on the Penn Arabic Treebank, using the ATB3-v3.2 80/10/10 split.
  3. Learning Morphology with Morfette (2008)
    Morfette learns morphological tagging and lemmatization jointly from annotated corpora. Two modules, each a maximum entropy classifier, predict the morphological tags and the lemmas. A third module combines their predictions into a probability distribution over tag-lemma pairs for each sentence. Lemmatization is…
  4. Adapting a WSJ-Trained Parser to Grammatically Noisy Text (2008)
    The paper makes a treebank of ungrammatical English by injecting one or two errors into Penn Treebank sentences and changing the tree only at the yield.
  5. Parser Evaluation and the BNC: Evaluating 4 constituency parsers with 3 metrics (2008)
    The paper builds a new test set of 1,000 hand-corrected British National Corpus parse trees, chosen so that each sentence has a verb that is unseen in the WSJ training sections.
  6. Adapting WSJ-Trained Parsers to the British National Corpus Using In-Domain Self-Training (2007)
    The question is whether a parser trained on WSJ text can be adapted to another domain without new annotation.
  7. Syntactic and Semantic Transfer with F-Structures (1998)
    The paper gives two ways to transfer between languages from LFG f-structures, the attribute-value syntax of LFG.

Co-authors (13)

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