Samia Touileb

• Barnes, Jeremy Claude; Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2019). Lexicon information in neural sentiment analysis: a multi-task learning approach, In Mareike Hartmann & Barbara Plank (ed.),  Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa).  Linköping University Electronic Press.  ISBN 978-91-7929-995-8.  Artikkel.  s 175 - 186 Show summary

  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.

• Rodina, Julia; Bakshandaeva, Daria; Fomin, Vadim; Kutuzov, Andrei; Touileb, Samia & Velldal, Erik (2019). Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian, In Nina Tahmasebi; Lars Borin; Adam Jatowt & Yang Xu (ed.),  Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change.  Association for Computational Linguistics.  ISBN 978-1-950737-31-4.  chapter.  s 202 - 209 Show summary

  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.

• Touileb, Samia; Pedersen, Truls Andre & Sjøvaag, Helle (2018). Automatic identification of unknown names with specific roles, In Beatrice Alex; Stefania Degaetano-Ortlieb; Anna Feldman; Anna Kazantseva; Nils Reiter & Stan Szpakowicz (ed.),  Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature.  Association for Computational Linguistics.  ISBN 978-1-948087-61-2.  150–158.  s 150 - 158
• Velldal, Erik; Øvrelid, Lilja; Bergem, Eivind Alexander; Stadsnes, Cathrine; Touileb, Samia & Jørgensen, Fredrik (2018). NoReC: The Norwegian Review Corpus, In Nicoletta Calzolari; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Koiti Hasida; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis & Takenobu Tokunaga (ed.),  Proceedings of the Eleventh International Conference on Language Resources and Evaluation.  European Language Resources Association.  ISBN 979-10-95546-00-9.  Article.  s 4186 - 4191 Show summary

  This paper presents the Norwegian Review Corpus (NoReC), created for training and evaluating models for document-level sentiment analysis. The full-text reviews have been collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. Each review is labeled with a manually assigned score of 1–6, as provided by the rating of the original author. This first release of the corpus comprises more than 35,000 reviews. It is distributed using the CoNLL-U format, pre-processed using UDPipe, along with a rich set of metadata. The work reported in this paper forms part of the SANT initiative (Sentiment Analysis for Norwegian Text), a project seeking to provide open resources and tools for sentiment analysis and opinion mining for Norwegian.

• Touileb, Samia & Steskal, Lubos (2016). ADIOS LDA: When Grammar Induction Meets Topic Modeling. NIK: Norsk Informatikkonferanse.  ISSN 1892-0713.
• Salway, Andrew & Touileb, Samia (2014). Applying grammar induction to text mining. Association for Computational Linguistics (ACL). Annual Meeting Conference Proceedings.  ISSN 0736-587X.  s 712- 717
• Salway, Andrew; Touileb, Samia & Tvinnereim, Endre (2014). Inducing Information Structures for Data-driven Text Analysis. Association for Computational Linguistics (ACL). Annual Meeting Conference Proceedings.  ISSN 0736-587X. Show summary

  We report ongoing work that is aiming to develop a data-driven approach to text analysis for computational social science. The novel feature is the use of a grammar induction algorithm to identify salient information structures from an unannotated text corpus. The structures provide richer representations of text content than keywords, by capturing patterning related to what is written about key terms. Here we show how information structures were induced from texts that record political negotiations, and how the structures were used in analyzing relations between countries and negotiation positions.

• Touileb, Samia & Salway, Andrew (2014). Constructions: a new unit of analysis for corpus-based discourse analysis, In Wirote Aroonmanakun; Thepchai Supnithi & Prachya Boonkwan (ed.),  Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation (PACLIC 28).  Chulalongkorn University.  ISBN 978-616-551-887-1.  -.  s 634 - 644

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• Sjøvaag, Helle; Pedersen, Truls Andre & Touileb, Samia (2018). Operationalising Diversity for Big Data Policy Research.
• Touileb, Samia; Pedersen, Truls Andre & Sjøvaag, Helle (2018). Automatically identifying names of unrecognized politicians.
• Pedersen, Truls Andre; Touileb, Samia & Sjøvaag, Helle (2017). Finding Voices in the Margins: Computer-Assisted Discovery of Naturally Belonging Names.
• Touileb, Samia; Elgesem, Dag & Salway, Andrew (2017). Automatically Inducing Information Structures. A Text Mining Approach Based on the Distributional Hypothesis.
• Touileb, Samia & Duarte, Katherine (2016). Getting to know large newsflows: Automatically induced information structures as keyphrases for news content analysis.
• Iversen, Magnus Hoem; Pedersen, Truls Andre; Stavelin, Eirik & Touileb, Samia (2015). Computer supported deliberation and argumentation online. Proposing a system for online argumentation..
• Touileb, Samia & Steskal, Lubos (2015). A computational approach to organize and analyze online communication data.
• Salway, Andrew; Hofland, Knut & Touileb, Samia (2013). Applying Corpus Techniques to Climate Change Blogs.
• Touileb, Samia (2013). Inducing local grammars from n-grams.
• Touileb, Samia; Elgesem, Dag & Steskal, Lubos (2012). Networks of texts and people.

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