Samia Touileb


2021

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Proceedings of the Sixth Arabic Natural Language Processing Workshop
Nizar Habash | Houda Bouamor | Hazem Hajj | Walid Magdy | Wajdi Zaghouani | Fethi Bougares | Nadi Tomeh | Ibrahim Abu Farha | Samia Touileb
Proceedings of the Sixth Arabic Natural Language Processing Workshop

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The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus
Samia Touileb | Jeremy Barnes
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Using Gender- and Polarity-Informed Models to Investigate Bias
Samia Touileb | Lilja Øvrelid | Erik Velldal
Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing

In this work we explore the effect of incorporating demographic metadata in a text classifier trained on top of a pre-trained transformer language model. More specifically, we add information about the gender of critics and book authors when classifying the polarity of book reviews, and the polarity of the reviews when classifying the genders of authors and critics. We use an existing data set of Norwegian book reviews with ratings by professional critics, which has also been augmented with gender information, and train a document-level sentiment classifier on top of a recently released Norwegian BERT-model. We show that gender-informed models obtain substantially higher accuracy, and that polarity-informed models obtain higher accuracy when classifying the genders of book authors. For this particular data set, we take this result as a confirmation of the gender bias in the underlying label distribution, but in other settings we believe a similar approach can be used for mitigating bias in the model.

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NorDial: A Preliminary Corpus of Written Norwegian Dialect Use
Jeremy Barnes | Petter Mæhlum | Samia Touileb
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere. There are, however, few available resources to study this variation and its change over time and in more informal areas, on social media. In this paper, we propose a first step to creating a corpus of dialectal variation of written Norwegian. We collect a small corpus of tweets and manually annotate them as Bokmål, Nynorsk, any dialect, or a mix. We further perform preliminary experiments with state-of-the-art models, as well as an analysis of the data to expand this corpus in the future. Finally, we make the annotations available for future work.

2020

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Named Entity Recognition without Labelled Data: A Weak Supervision Approach
Pierre Lison | Jeremy Barnes | Aliaksandr Hubin | Samia Touileb
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.

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LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier
Samia Touileb
Proceedings of the Fifth Arabic Natural Language Processing Workshop

This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a stacking classifier using Multinomial Naive Bayes, Linear SVC, and Logistic Regression classifiers as estimators; followed by a Logistic Regression as final estimator. Despite the fact that the results on the test set were low, with a macro F1 of 17.71, we were able to show that a simple approach can achieve comparable results to more sophisticated solutions. Moreover, the insights of our error analysis, and of the corpus content in general, can be used to develop and improve future systems.

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Identifying Sentiments in Algerian Code-switched User-generated Comments
Wafia Adouane | Samia Touileb | Jean-Philippe Bernardy
Proceedings of the 12th Language Resources and Evaluation Conference

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. We opted for this data domain because Algerian is a colloquial language with no existing freely available corpora. Moreover, we compiled sentiment lexicons of positive and negative unigrams and bigrams reflecting the code-switches present in the language. We compare the performance of four models on the task of identifying sentiments, and the results indicate that a CNN model trained end-to-end fits better our unedited code-switched and unbalanced data across the predefined sentiment classes. Additionally, injecting the lexicons as background knowledge to the model boosts its performance on the minority class with a gain of 10.54 points on the F-score. The results of our experiments can be used as a baseline for future research for Algerian sentiment analysis.

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Gender and sentiment, critics and authors: a dataset of Norwegian book reviews
Samia Touileb | Lilja Øvrelid | Erik Velldal
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing

Gender bias in models and datasets is widely studied in NLP. The focus has usually been on analysing how females and males express themselves, or how females and males are described. However, a less studied aspect is the combination of these two perspectives, how female and male describe the same or opposite gender. In this paper, we present a new gender annotated sentiment dataset of critics reviewing the works of female and male authors. We investigate if this newly annotated dataset contains differences in how the works of male and female authors are critiqued, in particular in terms of positive and negative sentiment. We also explore the differences in how this is done by male and female critics. We show that there are differences in how critics assess the works of authors of the same or opposite gender. For example, male critics rate crime novels written by females, and romantic and sentimental works written by males, more negatively.

2019

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Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian
Julia Rodina | Daria Bakshandaeva | Vadim Fomin | Andrey Kutuzov | Samia Touileb | Erik Velldal
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

We measure the intensity of diachronic semantic shifts in adjectives in English, Norwegian and Russian across 5 decades. This is done in order to test the hypothesis that evaluative adjectives are more prone to temporal semantic change. To this end, 6 different methods of quantifying semantic change are used. Frequency-controlled experimental results show that, depending on the particular method, evaluative adjectives either do not differ from other types of adjectives in terms of semantic change or appear to actually be less prone to shifting (particularly, to ‘jitter’-type shifting). Thus, in spite of many well-known examples of semantically changing evaluative adjectives (like ‘terrific’ or ‘incredible’), it seems that such cases are not specific to this particular type of words.

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Lexicon information in neural sentiment analysis: a multi-task learning approach
Jeremy Barnes | Samia Touileb | Lilja Øvrelid | Erik Velldal
Proceedings of the 22nd Nordic Conference on Computational Linguistics

This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.

2018

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Automatic identification of unknown names with specific roles
Samia Touileb | Truls Pedersen | Helle Sjøvaag
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Automatically identifying persons in a particular role within a large corpus can be a difficult task, especially if you don’t know who you are actually looking for. Resources compiling names of persons can be available, but no exhaustive lists exist. However, such lists usually contain known names that are “visible” in the national public sphere, and tend to ignore the marginal and international ones. In this article we propose a method for automatically generating suggestions of names found in a corpus of Norwegian news articles, and which “naturally” belong to a given initial list of members, and that were not known (compiled in a list) beforehand. The approach is based, in part, on the assumption that surface level syntactic features reveal parts of the underlying semantic content and can help uncover the structure of the language.

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NoReC: The Norwegian Review Corpus
Erik Velldal | Lilja Øvrelid | Eivind Alexander Bergem | Cathrine Stadsnes | Samia Touileb | Fredrik Jørgensen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2014

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Constructions: a New Unit of Analysis for Corpus-based Discourse Analysis
Samia Touileb | Andrew Salway
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

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Applying Grammar Induction to Text Mining
Andrew Salway | Samia Touileb
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Inducing Information Structures for Data-driven Text Analysis
Andrew Salway | Samia Touileb | Endre Tvinnereim
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science