Ramy Eskander

SLSA: A Sentiment Lexicon for Standard Arabic

Sentiment analysis has been a major area of interest, for which the existence of highquality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies. The paper presents a large-scale Standard Arabic Sentiment Lexicon (SLSA) that is publicly available for free and avoids the deficiencies in the current resources. SLSA has the highest up-to-date reported coverage. The construction of SLSA is based on linking the lexicon of AraMorph with SentiWordNet along with a few heuristics and powerful back-off. SLSA shows a relative improvement of 37.8% over a state-of-theart lexicon when tested for accuracy. It also outperforms it by an absolute 3.5% of F1-score when tested for sentiment analysis.

Automatic Transliteration of Romanized Dialectal Arabic

In this paper, we address the problem of converting Dialectal Arabic (DA) text that is written in the Latin script (called Arabizi) into Arabic script following the CODA convention for DA orthography. The presented system uses a finite state transducer trained at the character level to generate all possible transliterations for the input Arabizi words. We then filter the generated list using a DA morphological analyzer. After that we pick the best choice for each input word using a language model. We achieve an accuracy of 69.4% on an unseen test set compared to 63.1% using a system which represents a previously proposed approach.

Transliteration of Arabizi into Arabic Orthography: Developing a Parallel Annotated Arabizi-Arabic Script SMS/Chat Corpus

This paper describes the process of creating a novel resource, a parallel Arabizi-Arabic script corpus of SMS/Chat data. The language used in social media expresses many differences from other written genres: its vocabulary is informal with intentional deviations from standard orthography such as repeated letters for emphasis; typos and nonstandard abbreviations are common; and nonlinguistic content is written out, such as laughter, sound representations, and emoticons. This situation is exacerbated in the case of Arabic social media for two reasons. First, Arabic dialects, commonly used in social media, are quite different from Modern Standard Arabic phonologically, morphologically and lexically, and most importantly, they lack standard orthographies. Second, Arabic speakers in social media as well as discussion forums, SMS messaging and online chat often use a non-standard romanization called Arabizi. In the context of natural language processing of social media Arabic, transliterating from Arabizi of various dialects to Arabic script is a necessary step, since many of the existing state-of-the-art resources for Arabic dialect processing expect Arabic script input. The corpus described in this paper is expected to support Arabic NLP by providing this resource.

Foreign Words and the Automatic Processing of Arabic Social Media Text Written in Roman Script

Arabic on social media has all the properties of any language on social media that make it tough for natural language processing, plus some specific problems. These include diglossia, the use of an alternative alphabet (Roman), and code switching with foreign languages. In this paper, we present a system which can process Arabic written in Roman alphabet (“Arabizi”). It identifies whether each word is a foreign word or one of another four categories (Arabic, name, punctuation, sound), and transliterates Arabic words and names into the Arabic alphabet. We obtain an overall system performance of 83.8% on an unseen test set.

The Columbia System in the QALB-2014 Shared Task on Arabic Error Correction

The QALB-2014 shared task focuses on correcting errors in texts written in Modern Standard Arabic. In this paper, we describe the Columbia University entry in the shared task. Our system consists of several components that rely on machinelearning techniques and linguistic knowledge. We submitted three versions of the system: these share several core elements but each version also includes additional components. We describe our underlying approach and the special aspects of the different versions of our submission. Our system ranked first out of nine participating teams.

The Columbia University - New York University Abu Dhabi SIGMORPHON 2016 Morphological Reinflection Shared Task Submission

We present a high-level description and error analysis of the Columbia-NYUAD system for morphological reinflection, which builds on previous work on supervised morphological paradigm completion. Our system improved over the shared task baseline on some of the languages, reaching up to 30% absolute increase. Our ranking on average was 5th in Track 1, 8th in Track 2, and 3rd in Track 3.