Search Results for author: Wafia Adouane

Found 11 papers, 0 papers with code

Identifying Sentiments in Algerian Code-switched User-generated Comments

no code implementations LREC 2020 Wafia Adouane, Samia Touileb, Jean-Philippe Bernardy

We present in this paper our work on Algerian language, an under-resourced North African colloquial Arabic variety, for which we built a comparably large corpus of more than 36, 000 code-switched user-generated comments annotated for sentiments.

Sentiment Analysis

When is Multi-task Learning Beneficial for Low-Resource Noisy Code-switched User-generated Algerian Texts?

no code implementations LREC 2020 Wafia Adouane, Jean-Philippe Bernardy

Our empirical results show that multi-task learning is beneficial for some tasks in particular settings, and that the effect of each task on another, the order of the tasks, and the size of the training data of the task with more data do matter.

Data Augmentation Multi-Task Learning +3

Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data

no code implementations WS 2019 Wafia Adouane, Jean-Philippe Bernardy, Simon Dobnik

We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard.

Semantic Textual Similarity Spelling Correction

Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA

no code implementations WS 2019 Wafia Adouane, Jean-Philippe Bernardy, Simon Dobnik

We explore the extent to which neural networks can learn to identify semantically equivalent sentences from a small variable dataset using an end-to-end training.

Semantic Textual Similarity

Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts

no code implementations WS 2018 Wafia Adouane, Jean-Philippe Bernardy, Simon Dobnik

We explore the effect of injecting background knowledge to different deep neural network (DNN) configurations in order to mitigate the problem of the scarcity of annotated data when applying these models on datasets of low-resourced languages.

Word Embeddings

A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts

no code implementations WS 2018 Wafia Adouane, Simon Dobnik, Jean-Philippe Bernardy, Nasredine Semmar

This paper seeks to examine the effect of including background knowledge in the form of character pre-trained neural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources.

Language Identification Language Modelling +1

Identification of Languages in Algerian Arabic Multilingual Documents

no code implementations WS 2017 Wafia Adouane, Simon Dobnik

This paper presents a language identification system designed to detect the language of each word, in its context, in a multilingual documents as generated in social media by bilingual/multilingual communities, in our case speakers of Algerian Arabic.

Chunking General Classification +1

ASIREM Participation at the Discriminating Similar Languages Shared Task 2016

no code implementations WS 2016 Wafia Adouane, Nasredine Semmar, Richard Johansson

In sub-task 2, which deals with Arabic dialect identification, the system achieved its best performance using character-based n-grams (49. 67{\%} accuracy), ranking fourth in the closed track (the best result being 51. 16{\%}), and an accuracy of 53. 18{\%}, ranking first in the open track.

Dialect Identification Task 2

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