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Mona Diab

6 paper cards in Mova's library · 2006–2020

Mona Diab wrote 6 of the papers Mova reads, in 2006–2020, most of them on Treebanks, Part-of-speech tagging, Machine translation; often with Owen Rambow, Nizar Habash, Ryan Roth.

OpenAlex · ORCID · h-index 48 · 11047 citations · 293 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: Treebanks 1 · Part-of-speech tagging 1 · Machine translation 1

Papers

  1. A Multitask Learning Approach for Diacritic Restoration (2020)
    Diacritic restoration for Arabic (adding the short vowels to undiacritized text) is usually done by a character-level BiLSTM, which can lose word-level information.
  2. Committed Belief Tagging on the Factbank and LU Corpora: A Comparative Study (2015)
    Committed belief (CB) is how strongly a writer commits to a proposition. Two corpora label it: the LU corpus (16 documents, four genres, 3-way labels) and FactBank (newswire, about four times larger, factuality labels). The authors map both to a common 4-way scheme: committed belief, non-committed belief…
  3. MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic (2014)
    MADAMIRA merges two older Arabic tools, MADA (morphology and disambiguation) and AMIRA (tokenization, tagging, chunking), into one Java system.
  4. Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking (2008)
    MADA picks, for each Arabic word, one analysis from the candidates that the BAMA dictionary proposes. It scores each candidate by summing the weights of the features on which the candidate agrees with the model's prediction. The features are 14 morphological tags, each predicted by an SVM, two checks (spelling match…
  5. Semi-automatic error analysis for large-scale statistical machine translation (2007)
    The paper relates properties of the input documents to the translation quality of a large SMT system, scored per document by HTER.
  6. Parsing Arabic Dialects (2006)
    Levantine Arabic (LA) is spoken, unwritten, and has almost no treebank, while MSA has the Penn Arabic Treebank (about 17,600 training sentences).

Co-authors (16)

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