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Chatrine Qwaider "Deep Learning for Arabic Computational Linguistics (Sentiment Analysis as case study)" - University of Gothenburg, Sweden Till startsida
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Chatrine Qwaider "Deep Learning for Arabic Computational Linguistics (Sentiment Analysis as case study)"

Research profile seminar

Chatrine Qwaider "Deep Learning for Arabic Computational Linguistics (Sentiment Analysis as case study)"

The Seminar is part of Reading course on applying deep learning for Arabic Computational Linguistics. I present an overview of relevant literature on employing deep learning architectures for different Arabic NLP tasks like Language Identification , Sentiment Analysis, Entity Recognition and so on. However, Dialectal Arabic faces a number of challenges when it comes to NLP, resulting in weak performance systems and models. One of the reasons for this is that we try to first build models for Modern Standard Arabic and later use the models to predict the Dialectal Arabic, something which does not work well. In this talk, I pick Sentiment Analysis as a case study in order to show the power of deep learning for Dialectal Arabic. A mixed LSTM and CNN network is presented for SA, giving reasonable results. We also experiment with fine tuning the pertained language model BERT in order to build a classification model for Dialectal SA on it. Lastly, we compare both the traditional DL and BERT results.

Lecturer: Chatrine Qwaider

Date: 4/1/2020

Time: 1:15 PM - 3:00 PM

Categories: Linguistics

Organizer: CLASP

Location: Zoom: https://gu-se.zoom.us/j/504231211

Contact person: Stergios Chatzikyriakidis

Page Manager: |Last update: 12/19/2012
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