Mova0.5 pre-release⌕
☰
home / docs / papers / authors / Nasser Zalmout

Nasser Zalmout

5 paper cards in Mova's library · 2017–2020

Nasser Zalmout wrote 5 of the papers Mova reads, in 2017–2020, most of them on Part-of-speech tagging; often with Nizar Habash, Alexander Erdmann, Salam Khalifa.

OpenAlex · h-index 16 · 803 citations · 43 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

Papers

  1. Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging (2020)
    Arabic lemmas and diacritized forms are open-vocabulary and best modelled on characters, while POS and the fourteen closed features are word-level tags.
  2. Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods (2020)
    This is the first full morphological analysis and disambiguation system for Gulf Arabic (Emirati data). The analyser for Gulf Arabic is not written by hand: it is built by paradigm completion from the training split of the Annotated Gumar Corpus (about 202,000 tokens, eight novel excerpts). Two hand-built analysers…
  3. Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling (2019)
    The authors tag fourteen Arabic morphological features (POS, aspect, case, gender, and others) with one Bi-LSTM whose hidden layers are shared across features, instead of one model per feature.
  4. Noise-Robust Morphological Disambiguation for Dialectal Arabic (2018)
    The paper builds a morphological disambiguator for Egyptian Arabic, where spelling is unstandardised and most words have many analyses (about 24 per word, against about 12 in MSA).
  5. Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic (2017)
    A morphological analyzer gives several readings for each Arabic word when taken out of context, and the task is to choose the right one in context.

Co-authors (3)

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/authors/nasser-zalmout/