Ramy Eskander


2021

pdf bib
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors
Ramy Eskander | Cass Lowry | Sujay Khandagale | Francesca Callejas | Judith Klavans | Maria Polinsky | Smaranda Muresan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

pdf bib
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios
Ramy Eskander | Smaranda Muresan | Michael Collins
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We describe a fully unsupervised cross-lingual transfer approach for part-of-speech (POS) tagging under a truly low resource scenario. We assume access to parallel translations between the target language and one or more source languages for which POS taggers are available. We use the Bible as parallel data in our experiments: small size, out-of-domain and covering many diverse languages. Our approach innovates in three ways: 1) a robust approach of selecting training instances via cross-lingual annotation projection that exploits best practices of unsupervised type and token constraints, word-alignment confidence and density of projected POS, 2) a Bi-LSTM architecture that uses contextualized word embeddings, affix embeddings and hierarchical Brown clusters, and 3) an evaluation on 12 diverse languages in terms of language family and morphological typology. In spite of the use of limited and out-of-domain parallel data, our experiments demonstrate significant improvements in accuracy over previous work. In addition, we show that using multi-source information, either via projection or output combination, improves the performance for most target languages.

pdf bib
MATERIALizing Cross-Language Information Retrieval: A Snapshot
Petra Galuscakova | Douglas Oard | Joe Barrow | Suraj Nair | Shing Han-Chin | Elena Zotkina | Ramy Eskander | Rui Zhang
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems’ abilities to find Lithuanian documents based on English queries. Subsequently, both the Lithuanian test collection and results from all three teams were made available for detailed analysis. This paper capitalizes on that opportunity to begin to look at what’s working well at this stage of the program, and to identify some promising directions for future work.

bib
An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages
Aquia Richburg | Ramy Eskander | Smaranda Muresan | Marine Carpuat
Proceedings of the The Fourth Widening Natural Language Processing Workshop

Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. We conduct a controlled comparison of subword segmentation strategies for translating two low-resource morphologically rich languages (Swahili and Turkish) into English. We show that segmentations based on a unigram language model (Kudo, 2018) yield comparable BLEU and better recall for translating rare source words than BPE.

pdf bib
MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation
Ramy Eskander | Francesca Callejas | Elizabeth Nichols | Judith Klavans | Smaranda Muresan
Proceedings of the 12th Language Resources and Evaluation Conference

Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).

2019

pdf bib
Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages
Ramy Eskander | Judith Klavans | Smaranda Muresan
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme complexity and to the word class “squish”. In addition, many of these polysynthetic languages are low-resource. We propose unsupervised approaches for morphological segmentation of low-resource polysynthetic languages based on Adaptor Grammars (AG) (Eskander et al., 2016). We experiment with four languages from the Uto-Aztecan family. Our AG-based approaches outperform other unsupervised approaches and show promise when compared to supervised methods, outperforming them on two of the four languages.

2018

pdf bib
SentiArabic: A Sentiment Analyzer for Standard Arabic
Ramy Eskander
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Unified Guidelines and Resources for Arabic Dialect Orthography
Nizar Habash | Fadhl Eryani | Salam Khalifa | Owen Rambow | Dana Abdulrahim | Alexander Erdmann | Reem Faraj | Wajdi Zaghouani | Houda Bouamor | Nasser Zalmout | Sara Hassan | Faisal Al-Shargi | Sakhar Alkhereyf | Basma Abdulkareem | Ramy Eskander | Mohammad Salameh | Hind Saddiki
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Automatically Tailoring Unsupervised Morphological Segmentation to the Language
Ramy Eskander | Owen Rambow | Smaranda Muresan
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

Morphological segmentation is beneficial for several natural language processing tasks dealing with large vocabularies. Unsupervised methods for morphological segmentation are essential for handling a diverse set of languages, including low-resource languages. Eskander et al. (2016) introduced a Language Independent Morphological Segmenter (LIMS) using Adaptor Grammars (AG) based on the best-on-average performing AG configuration. However, while LIMS worked best on average and outperforms other state-of-the-art unsupervised morphological segmentation approaches, it did not provide the optimal AG configuration for five out of the six languages. We propose two language-independent classifiers that enable the selection of the optimal or nearly-optimal configuration for the morphological segmentation of unseen languages.

2016

pdf bib
The Columbia University - New York University Abu Dhabi SIGMORPHON 2016 Morphological Reinflection Shared Task Submission
Dima Taji | Ramy Eskander | Nizar Habash | Owen Rambow
Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

pdf bib
Extending the Use of Adaptor Grammars for Unsupervised Morphological Segmentation of Unseen Languages
Ramy Eskander | Owen Rambow | Tianchun Yang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We investigate using Adaptor Grammars for unsupervised morphological segmentation. Using six development languages, we investigate in detail different grammars, the use of morphological knowledge from outside sources, and the use of a cascaded architecture. Using cross-validation on our development languages, we propose a system which is language-independent. We show that it outperforms two state-of-the-art systems on 5 out of 6 languages.

pdf bib
Creating Resources for Dialectal Arabic from a Single Annotation: A Case Study on Egyptian and Levantine
Ramy Eskander | Nizar Habash | Owen Rambow | Arfath Pasha
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Arabic dialects present a special problem for natural language processing because there are few resources, they have no standard orthography, and have not been studied much. However, as more and more written dialectal Arabic is found in social media, NLP for Arabic dialects becomes an important goal. We present a methodology for creating a morphological analyzer and a morphological tagger for dialectal Arabic, and we illustrate it on Egyptian and Levantine Arabic. To our knowledge, these are the first analyzer and tagger for Levantine.

pdf bib
Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic
Faisal Al-Shargi | Aidan Kaplan | Ramy Eskander | Nizar Habash | Owen Rambow
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present new language resources for Moroccan and Sanaani Yemeni Arabic. The resources include corpora for each dialect which have been morphologically annotated, and morphological analyzers for each dialect which are derived from these corpora. These are the first sets of resources for Moroccan and Yemeni Arabic. The resources will be made available to the public.

pdf bib
SPLIT: Smart Preprocessing (Quasi) Language Independent Tool
Mohamed Al-Badrashiny | Arfath Pasha | Mona Diab | Nizar Habash | Owen Rambow | Wael Salloum | Ramy Eskander
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Text preprocessing is an important and necessary task for all NLP applications. A simple variation in any preprocessing step may drastically affect the final results. Moreover replicability and comparability, as much as feasible, is one of the goals of our scientific enterprise, thus building systems that can ensure the consistency in our various pipelines would contribute significantly to our goals. The problem has become quite pronounced with the abundance of NLP tools becoming more and more available yet with different levels of specifications. In this paper, we present a dynamic unified preprocessing framework and tool, SPLIT, that is highly configurable based on user requirements which serves as a preprocessing tool for several tools at once. SPLIT aims to standardize the implementations of the most important preprocessing steps by allowing for a unified API that could be exchanged across different researchers to ensure complete transparency in replication. The user is able to select the required preprocessing tasks among a long list of preprocessing steps. The user is also able to specify the order of execution which in turn affects the final preprocessing output.

2015

pdf bib
SLSA: A Sentiment Lexicon for Standard Arabic
Ramy Eskander | Owen Rambow
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

pdf bib
Developing an Egyptian Arabic Treebank: Impact of Dialectal Morphology on Annotation and Tool Development
Mohamed Maamouri | Ann Bies | Seth Kulick | Michael Ciul | Nizar Habash | Ramy Eskander
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes the parallel development of an Egyptian Arabic Treebank and a morphological analyzer for Egyptian Arabic (CALIMA). By the very nature of Egyptian Arabic, the data collected is informal, for example Discussion Forum text, which we use for the treebank discussed here. In addition, Egyptian Arabic, like other Arabic dialects, is sufficiently different from Modern Standard Arabic (MSA) that tools and techniques developed for MSA cannot be simply transferred over to work on Egyptian Arabic work. In particular, a morphological analyzer for Egyptian Arabic is needed to mediate between the written text and the segmented, vocalized form used for the syntactic trees. This led to the necessity of a feedback loop between the treebank team and the analyzer team, as improvements in each area were fed to the other. Therefore, by necessity, there needed to be close cooperation between the annotation team and the tool development team, which was to their mutual benefit. Collaboration on this type of challenge, where tools and resources are limited, proved to be remarkably synergistic and opens the way to further fruitful work on Arabic dialects.

pdf bib
Tharwa: A Large Scale Dialectal Arabic - Standard Arabic - English Lexicon
Mona Diab | Mohamed Al-Badrashiny | Maryam Aminian | Mohammed Attia | Heba Elfardy | Nizar Habash | Abdelati Hawwari | Wael Salloum | Pradeep Dasigi | Ramy Eskander
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We introduce an electronic three-way lexicon, Tharwa, comprising Dialectal Arabic, Modern Standard Arabic and English correspondents. The paper focuses on Egyptian Arabic as the first pilot dialect for the resource, with plans to expand to other dialects of Arabic in later phases of the project. We describe Tharwa’s creation process and report on its current status. The lexical entries are augmented with various elements of linguistic information such as POS, gender, rationality, number, and root and pattern information. The lexicon is based on a compilation of information from both monolingual and bilingual existing resources such as paper dictionaries and electronic, corpus-based dictionaries. Multiple levels of quality checks are performed on the output of each step in the creation process. The importance of this lexicon lies in the fact that it is the first resource of its kind bridging multiple variants of Arabic with English. Furthermore, it is a wide coverage lexical resource containing over 73,000 Egyptian entries. Tharwa is publicly available. We believe it will have a significant impact on both Theoretical Linguistics as well as Computational Linguistics research.

pdf bib
MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
Arfath Pasha | Mohamed Al-Badrashiny | Mona Diab | Ahmed El Kholy | Ramy Eskander | Nizar Habash | Manoj Pooleery | Owen Rambow | Ryan Roth
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we present MADAMIRA, a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab et al., 2007). MADAMIRA improves upon the two systems with a more streamlined Java implementation that is more robust, portable, extensible, and is faster than its ancestors by more than an order of magnitude. We also discuss an online demo (see http://nlp.ldeo.columbia.edu/madamira/) that highlights these aspects.

pdf bib
Automatic Transliteration of Romanized Dialectal Arabic
Mohamed Al-Badrashiny | Ramy Eskander | Nizar Habash | Owen Rambow
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

pdf bib
Transliteration of Arabizi into Arabic Orthography: Developing a Parallel Annotated Arabizi-Arabic Script SMS/Chat Corpus
Ann Bies | Zhiyi Song | Mohamed Maamouri | Stephen Grimes | Haejoong Lee | Jonathan Wright | Stephanie Strassel | Nizar Habash | Ramy Eskander | Owen Rambow
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

pdf bib
A Pipeline Approach to Supervised Error Correction for the QALB-2014 Shared Task
Nadi Tomeh | Nizar Habash | Ramy Eskander | Joseph Le Roux
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

pdf bib
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)

pdf bib
Foreign Words and the Automatic Processing of Arabic Social Media Text Written in Roman Script
Ramy Eskander | Mohamed Al-Badrashiny | Nizar Habash | Owen Rambow
Proceedings of the First Workshop on Computational Approaches to Code Switching

2013

pdf bib
Automatic Extraction of Morphological Lexicons from Morphologically Annotated Corpora
Ramy Eskander | Nizar Habash | Owen Rambow
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

pdf bib
Morphological Analysis and Disambiguation for Dialectal Arabic
Nizar Habash | Ryan Roth | Owen Rambow | Ramy Eskander | Nadi Tomeh
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Processing Spontaneous Orthography
Ramy Eskander | Nizar Habash | Owen Rambow | Nadi Tomeh
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Automatic Correction and Extension of Morphological Annotations
Ramy Eskander | Nizar Habash | Ann Bies | Seth Kulick | Mohamed Maamouri
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

2012

pdf bib
A Morphological Analyzer for Egyptian Arabic
Nizar Habash | Ramy Eskander | Abdelati Hawwari
Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology