Papers by Ramy Eskander
SentiArabic: A Sentiment Analyzer for Standard Arabic (L18-1)
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| Challenge: | Sentiment analysis is a process of applying computational approaches to identify attitudes, emotions and opinions in text, speech and visual data. |
| Approach: | They propose a sentiment analyzer that identifies the overall contextual polarity for Arabic text. |
| Outcome: | The proposed system achieves an F-score of 76.5% when evaluated on a blind test set. |
MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation (2020.lrec-1)
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| Challenge: | Unsupervised morphological segmentation is beneficial for many natural language processing tasks. |
| Approach: | They propose a framework for unsupervised morphological segmentation that uses Adaptor Grammars. |
| Outcome: | The proposed framework achieves state-of-the-art results across languages of different typologies, from fusional to polysynthetic and from high-resource to low-resourced. |
Unified Guidelines and Resources for Arabic Dialect Orthography (L18-1)
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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
| Challenge: | Existing efforts to conventionalize the dialectal orthography of Arabic have focused on specific dialects and made ad hoc decisions. |
| Approach: | They propose a set of guidelines and meta-guidelines for conventional orthography of Arabic dialects . they apply them to 28 Arab city dialects from Rabat to Muscat . |
| Outcome: | The proposed guidelines and resources are being used by three large Arabic dialect processing projects in three universities. |
Towards Unsupervised Morphological Analysis of Polysynthetic Languages (2022.aacl-short)
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Sujay Khandagale, Yoann Léveillé, Samuel Miller, Derek Pham, Ramy Eskander, Cass Lowry, Richard Compton, Judith Klavans, Maria Polinsky, Smaranda Muresan
| Challenge: | Polysynthetic languages are low-resource, lacking large scale annotated datasets needed to build and/or evaluate computational models. |
| Approach: | They propose to use linguistic priors to help with morphological segmentation and part-of-speech tagging tasks for Adyghe and Inuktitut . |
| Outcome: | The proposed methods improve morphological segmentation and part-of-speech tagging tasks on Adyghe and Inuktitut. |
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios (2020.emnlp-main)
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| Challenge: | a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance . |
| Approach: | They propose an unsupervised cross-lingual transfer approach for part-of-speech tagging . they use the Bible as parallel data to learn POS taggers for target languages . |
| Outcome: | The proposed approach improves accuracy on 12 diverse languages . the Bible is used as a parallel corpus for the study . |
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors (2021.findings-acl)
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Ramy Eskander, Cass Lowry, Sujay Khandagale, Francesca Callejas, Judith Klavans, Maria Polinsky, Smaranda Muresan
| Challenge: | Unsupervised morphological segmentation is an essential subtask in many natural language processing applications. |
| Approach: | They introduce two types of priors: grammar definition and linguist-provided affixes . they show that priors boost morphological segmentation performance in a minimally-supervised manner . |
| Outcome: | The proposed priors achieve 8.9% and 34.2% error reductions over the state-of-the-art unsupervised system. |
Unsupervised Stem-based Cross-lingual Part-of-Speech Tagging for Morphologically Rich Low-Resource Languages (2022.naacl-main)
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| Challenge: | Low-resource languages lack annotated data even for basic syntactic information such as parts of speech. |
| Approach: | They propose an unsupervised cross-lingual approach for POS tagging for low-resource languages of rich morphology . they further investigate morpheme-level alignment and projection and use of linguistic priors for morphological segmentation . |
| Outcome: | The proposed approach outperforms the word-based approach and outperfies word-driven approaches. |