Noura Farra


2019

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Predicting the Type and Target of Offensive Posts in Social Media
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Sara Rosenthal | Noura Farra | Ritesh Kumar
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.

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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Sara Rosenthal | Noura Farra | Ritesh Kumar
Proceedings of the 13th International Workshop on Semantic Evaluation

We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.

2017

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SMARTies: Sentiment Models for Arabic Target entities
Noura Farra | Kathy McKeown
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources. We present a system that is applied to complex posts written in response to Arabic newspaper articles. Our goal is to identify important entity “targets” within the post along with the polarity expressed about each target. We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.

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SemEval-2017 Task 4: Sentiment Analysis in Twitter
Sara Rosenthal | Noura Farra | Preslav Nakov
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

2015

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Scoring Persuasive Essays Using Opinions and their Targets
Noura Farra | Swapna Somasundaran | Jill Burstein
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

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Annotating Targets of Opinions in Arabic using Crowdsourcing
Noura Farra | Kathy McKeown | Nizar Habash
Proceedings of the Second Workshop on Arabic Natural Language Processing

2014

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Generalized Character-Level Spelling Error Correction
Noura Farra | Nadi Tomeh | Alla Rozovskaya | Nizar Habash
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction
Alla Rozovskaya | Nizar Habash | Ramy Eskander | Noura Farra | Wael Salloum
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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Large Scale Arabic Error Annotation: Guidelines and Framework
Wajdi Zaghouani | Behrang Mohit | Nizar Habash | Ossama Obeid | Nadi Tomeh | Alla Rozovskaya | Noura Farra | Sarah Alkuhlani | Kemal Oflazer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.

2013

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Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition
Nadi Tomeh | Nizar Habash | Ryan Roth | Noura Farra | Pradeep Dasigi | Mona Diab
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)