Noura Farra نورا فرا
I am a PhD candidate at Columbia University in the Computer Science department,
where I am part of the Natural Language Processing group since Fall 2012.
My research advisor is Prof. Kathleen McKeown.
I received both my Bachelors and Masters degrees in Electrical and Computer Engineering from the American University of Beirut in Lebanon.
Research
Sentiment and Opinion Analysis for Low Resource Languages
My research interests lie in the fields of Natural Language Processing and Computational Linguistics, and more broadly, applied Machine Learning. I work on
sentiment and opinion analysis in multilingual and low resource languages. I am interested in creating resources and models for topic-directed
directed sentiment analysis in these languages. I am applying my work to social, political, and natural disaster data in newswire, online discussion, and social media genres,
in high-resource, low-resource, and morphologically rich languages.
Previously, I worked on other projects:
Spelling Error Correction and Noisy Text Normalization
From 2012-2014, I worked with Prof. Nizar Habash on different methods for correcting spelling errors in text, which we applied for normalizing noisy Egyptian dialect text. This work was part of the QALB قلب Arabic error annotation and correction project.
Emotion Recognition and Sensor Data Processing
In my master's dissertation, I proposed a model and user study to recognize emotions using real-world data from different sources. During my undergraduate and masters, I also worked on various projects involving sensor data processing, such as posture monitoring and gesture recognition.
Publications
2016
2015
Noura Farra, Swapna Somasundaran, and Jill Burstein. 2015. Scoring Persuasive Essays Using Opinions and their Targets. Proceedings of the NAACL-2015 Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2015).
2014
Wajdi Zaghouani, Behrang Mohit, Nizar Habash, Oussama Obeid, Nadi Tomeh, Alla Rozovskaya, Noura Farra, Sara Alkuhlani, and Kamal Oflazer. 2014. Large Scale Arabic Error Annotation: Guidelines and Framework . In Proceedings of LREC.
Alla Rozovskaya, Nizar Habash, Ramy Eskander, Noura Farra, and Wael Salloum. 2014. The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction . Proceedings of the EMNLP Workshop on Arabic Natural Language Processing: QALB Shared Task. 2014.
2013
Nadi Tomeh, Nizar Habash, Ryan Roth, Noura Farra, Pradeep Dasigi, and Mona Diab. 2013. Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition . In Proceedings of the Conference of the Association for Computational Linguistics (ACL) .
2011
Noura Farra, Bilal El-Sayed, Nadine Moacdieh, Hazem Hajj, Ziad Hajj, and Rachid Haidar. 2011. A Mobile Sensing and Imaging System for Real-time Monitoring of Spine Health . In Journal of Medical Imaging and Health Informatics.
Bilal El-Sayed, Noura Farra, Nadine Moacdieh, Hazem Hajj, Ziad Hajj, and Rachid Haidar. A Novel Mobile Wireless Sensing System for Real-Time Monitoring of Spine Stress. In Proceedings of the 1st Middle East Conference on Biomedical Engineering.
2010
Noura Farra, Nadine Moacdieh, and Bilal El-Sayed. 2010. A Novel Image Processing Technique for Scoliosis Diagnosis. In Proceedings of the Student Conference at the American University of Beirut, Faculty of Engineering and Architecture.
Internships
I interned at Google in summer of 2015, where I was part of the team working on identifying political issues in quotes by presidential candidates.
Teaching
In Spring 2016, I was a TA for the Natural Language Processing course taught by Prof. Dragomir Radev.
In Spring 2015, I was a TA for the Natural Language Processing course taught by Prof. Michael Collins.
Professional Service
Contact
noura (at) cs (dot) columbia (dot) edu
Columbia University
726 Schapiro CEPSR
New York, NY
CV
Other
Check out the talk I gave to the amazing high school Girls Who Code class at CUNY in summer of 2016.
Check out the "Browser Topic Reader" that we developed for our 'Digitally Mediated Storytelling' class and which was showcased at the Columbia journalism school.