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Hussein Al-Natsheh

Hussein Al-Natsheh has 4 papers in our library, in 2019–2021, most of them on Semitic languages; often with Bashar Talafha, Haitham Seelawi, Wael Farhan.

4 papers in our library · 2019–2021

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📄 4 works in our library · 🔗 Cited 82 times in our library · ⭐ Paper Stars ranking: #5991

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

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 4 works

 Works by year

2020s

20211 paper20201 paper

2010s

20192 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Multi-dialect Arabic BERT for Country-level Dialect Identification (2020 · 77 citations)
    The paper describes the system by team Mawdoo3 AI that took first place in subtask 1 of the NADI shared task.
  2. ALUE: Arabic Language Understanding Evaluation (2021 · 48 citations)
    Arabic NLP had no shared, GLUE-style benchmark for comparing general-purpose language understanding models, especially multi-task ones.
  3. Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning (2019 · 23 citations)
    The paper describes Mawdoo3 AI's entry to sub-task 1 of the MADAR shared task (WANLP 2019). The task is fine-grained Arabic dialect identification: each sentence from the travel domain is assigned to one of 26 classes, which are 25 city dialects plus MSA. The team represents each sentence with TF–IDF vectors over…
  4. Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification (2019 · 19 citations)
    The paper addresses user-level Arabic dialect identification for MADAR shared subtask-2. Given up to 100 tweets per Twitter user, the system must assign one of 21 country dialects. The data is difficult in several ways. Classes are heavily skewed toward Saudi Arabia. Most tweets are retweets, so the text often comes…

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 82 times (of the 149,439 papers whose bibliographies we read); 57 citing papers with a page here →.

Updated

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