Atefeh Farzindar


2015

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Applications of Social Media Text Analysis
Atefeh Farzindar | Diana Inkpen
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Analyzing social media texts is a complex problem that becomes difficult to address using traditional Natural Language Processing (NLP) methods. Our tutorial focuses on presenting new methods for NLP tasks and applications that work on noisy and informal texts, such as the ones from social media.Automatic processing of large collections of social media texts is important because they contain a lot of useful information, due to the in-creasing popularity of all types of social media. Use of social media and messaging apps grew 203 percent year-on-year in 2013, with overall app use rising 115 percent over the same period, as reported by Statista, citing data from Flurry Analytics. This growth means that 1.61 billion people are now active in social media around the world and this is expected to advance to 2 billion users in 2016, led by India. The research shows that consumers are now spending daily 5.6 hours on digital media including social media and mo-bile internet usage.At the heart of this interest is the ability for users to create and share content via a variety of platforms such as blogs, micro-blogs, collaborative wikis, multimedia sharing sites, social net-working sites. The unprecedented volume and variety of user-generated content, as well as the user interaction network constitute new opportunities for understanding social behavior and building socially intelligent systems. Therefore it is important to investigate methods for knowledge extraction from social media data. Furthermore, we can use this information to detect and retrieve more related content about events, such as photos and video clips that have caption texts.

2014

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Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)
Atefeh Farzindar | Diana Inkpen | Michael Gamon | Meena Nagarajan
Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)

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Collaboratively Constructed Linguistic Resources for Language Variants and their Exploitation in NLP Application – the case of Tunisian Arabic and the Social Media
Fatiha Sadat | Fatma Mallek | Mohamed Boudabous | Rahma Sellami | Atefeh Farzindar
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

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Automatic Identification of Arabic Language Varieties and Dialects in Social Media
Fatiha Sadat | Farzindar Kazemi | Atefeh Farzindar
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

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Hashtag Occurrences, Layout and Translation: A Corpus-driven Analysis of Tweets Published by the Canadian Government
Fabrizio Gotti | Phillippe Langlais | Atefeh Farzindar
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present an aligned bilingual corpus of 8758 tweet pairs in French and English, derived from Canadian government agencies. Hashtags appear in a tweet’s prologue, announcing its topic, or in the tweet’s text in lieu of traditional words, or in an epilogue. Hashtags are words prefixed with a pound sign in 80% of the cases. The rest is mostly multiword hashtags, for which we describe a segmentation algorithm. A manual analysis of the bilingual alignment of 5000 hashtags shows that 5% (French) to 18% (English) of them don’t have a counterpart in their containing tweet’s translation. This analysis shows that 80% of multiword hashtags are correctly translated by humans, and that the mistranslation of the rest may be due to incomplete translation directives regarding social media. We show how these resources and their analysis can guide the design of a machine translation pipeline, and its evaluation. A baseline system implementing a tweet-specific tokenizer yields promising results. The system is improved by translating epilogues, prologues, and text separately. We attempt to feed the SMT engine with the original hashtag and some alternatives (“dehashed” version or a segmented version of multiword hashtags), but translation quality improves at the cost of hashtag recall.

2013

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Proceedings of the Workshop on Language Analysis in Social Media
Cristian Danescu-Niculescu-Mizil | Atefeh Farzindar | Michael Gamon | Diana Inkpen | Meena Nagarajan
Proceedings of the Workshop on Language Analysis in Social Media

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Translating Government Agencies’ Tweet Feeds: Specificities, Problems and (a few) Solutions
Fabrizio Gotti | Philippe Langlais | Atefeh Farzindar
Proceedings of the Workshop on Language Analysis in Social Media

2012

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Proceedings of the Workshop on Semantic Analysis in Social Media
Atefeh Farzindar | Diana Inkpen
Proceedings of the Workshop on Semantic Analysis in Social Media

2004

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Legal Text Summarization by Exploration of the Thematic Structure and Argumentative Roles
Atefeh Farzindar | Guy Lapalme
Text Summarization Branches Out