Search Results for author: Hussein T. Al-Natsheh

Found 7 papers, 2 papers with code

SPARTA: Speaker Profiling for ARabic TAlk

no code implementations13 Dec 2020 Wael Farhan, Muhy Eddin Za'ter, Qusai Abu Obaidah, Hisham al Bataineh, Zyad Sober, Hussein T. Al-Natsheh

LSTM and CNN networks were implemented using raw features: MFCC and MEL, where FCNN was explored on the pre-trained vectors while varying the hyper-parameters of these networks to obtain the best results for each dataset and task.

Multi-Task Learning Speaker Profiling +1

Multi-Dialect Arabic BERT for Country-Level Dialect Identification

1 code implementation COLING (WANLP) 2020 Bashar Talafha, Mohammad Ali, Muhy Eddin Za'ter, Haitham Seelawi, Ibraheem Tuffaha, Mostafa Samir, Wael Farhan, Hussein T. Al-Natsheh

Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26. 78% on the subtask at hand.

Dialect Identification Language Modelling

Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions

no code implementations19 Sep 2019 Hesham Al-Bataineh, Wael Farhan, Ahmad Mustafa, Haitham Seelawi, Hussein T. Al-Natsheh

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora.

Community Question Answering Question Similarity +2

NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic

no code implementations12 Sep 2019 Haitham Seelawi, Ahmad Mustafa, Hesham Al-Bataineh, Wael Farhan, Hussein T. Al-Natsheh

Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems.

Question Answering Question Similarity +2

Metadata Enrichment of Multi-Disciplinary Digital Library: A Semantic-based Approach

no code implementations21 Jun 2018 Hussein T. Al-Natsheh, Lucie Martinet, Fabrice Muhlenbach, Fabien Rico, Djamel A. Zighed

The approach starts by learning from a standard scientific categorization and a sample of topic tagged articles to find semantically relevant articles and enrich its metadata accordingly.

Information Retrieval

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