Hamdy Mubarak


2020

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Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection
Hend Al-Khalifa | Walid Magdy | Kareem Darwish | Tamer Elsayed | Hamdy Mubarak
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

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Overview of OSACT4 Arabic Offensive Language Detection Shared Task
Hamdy Mubarak | Kareem Darwish | Walid Magdy | Tamer Elsayed | Hend Al-Khalifa
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4). There were two subtasks, namely: Subtask A, involving the detection of offensive language, which contains unacceptable or vulgar content in addition to any kind of explicit or implicit insults or attacks against individuals or groups; and Subtask B, involving the detection of hate speech, which contains insults or threats targeting a group based on their nationality, ethnicity, race, gender, political or sport affiliation, religious belief, or other common characteristics. In total, 40 teams signed up to participate in Subtask A, and 14 of them submitted test runs. For Subtask B, 33 teams signed up to participate and 13 of them submitted runs. We present and analyze all submissions in this paper.

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ALT Submission for OSACT Shared Task on Offensive Language Detection
Sabit Hassan | Younes Samih | Hamdy Mubarak | Ahmed Abdelali | Ammar Rashed | Shammur Absar Chowdhury
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.

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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
Marcos Zampieri | Preslav Nakov | Sara Rosenthal | Pepa Atanasova | Georgi Karadzhov | Hamdy Mubarak | Leon Derczynski | Zeses Pitenis | Çağrı Çöltekin
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.

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ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media
Sabit Hassan | Younes Samih | Hamdy Mubarak | Ahmed Abdelali
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90% macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.

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A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection
Shammur Absar Chowdhury | Hamdy Mubarak | Ahmed Abdelali | Soon-gyo Jung | Bernard J. Jansen | Joni Salminen
Proceedings of the 12th Language Resources and Evaluation Conference

Access to social media often enables users to engage in conversation with limited accountability. This allows a user to share their opinions and ideology, especially regarding public content, occasionally adopting offensive language. This may encourage hate crimes or cause mental harm to targeted individuals or groups. Hence, it is important to detect offensive comments in social media platforms. Typically, most studies focus on offensive commenting in one platform only, even though the problem of offensive language is observed across multiple platforms. Therefore, in this paper, we introduce and make publicly available a new dialectal Arabic news comment dataset, collected from multiple social media platforms, including Twitter, Facebook, and YouTube. We follow two-step crowd-annotator selection criteria for low-representative language annotation task in a crowdsourcing platform. Furthermore, we analyze the distinctive lexical content along with the use of emojis in offensive comments. We train and evaluate the classifiers using the annotated multi-platform dataset along with other publicly available data. Our results highlight the importance of multiple platform dataset for (a) cross-platform, (b) cross-domain, and (c) cross-dialect generalization of classifier performance.

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Constructing a Bilingual Corpus of Parallel Tweets
Hamdy Mubarak | Sabit Hassan | Ahmed Abdelali
Proceedings of the 13th Workshop on Building and Using Comparable Corpora

In a bid to reach a larger and more diverse audience, Twitter users often post parallel tweets—tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. In this paper, we introduce a generic method for collecting parallel tweets. Using this method, we collect a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabictweets regularly. Since our method is generic, it can also be used for collecting parallel tweets that cover less-resourced languages such as Serbian and Urdu. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets. This latter information can also be useful for author profiling tasks.

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Arabic Curriculum Analysis
Hamdy Mubarak | Shimaa Amer | Ahmed Abdelali | Kareem Darwish
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes. In this paper, we present a system to analyze Arabic curricula and provide insights into their content. It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected. Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.

2019

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A System for Diacritizing Four Varieties of Arabic
Hamdy Mubarak | Ahmed Abdelali | Kareem Darwish | Mohamed Eldesouki | Younes Samih | Hassan Sajjad
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Short vowels, aka diacritics, are more often omitted when writing different varieties of Arabic including Modern Standard Arabic (MSA), Classical Arabic (CA), and Dialectal Arabic (DA). However, diacritics are required to properly pronounce words, which makes diacritic restoration (a.k.a. diacritization) essential for language learning and text-to-speech applications. In this paper, we present a system for diacritizing MSA, CA, and two varieties of DA, namely Moroccan and Tunisian. The system uses a character level sequence-to-sequence deep learning model that requires no feature engineering and beats all previous SOTA systems for all the Arabic varieties that we test on.

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POS Tagging for Improving Code-Switching Identification in Arabic
Mohammed Attia | Younes Samih | Ali Elkahky | Hamdy Mubarak | Ahmed Abdelali | Kareem Darwish
Proceedings of the Fourth Arabic Natural Language Processing Workshop

When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language. This paper explores the possibility of utilizing this pattern in improving code-switching identification between Modern Standard Arabic (MSA) and Egyptian Arabic (EA). We try to answer the question of how strong is the POS signal in word-level code-switching identification. We build a deep learning model enriched with linguistic features (including POS tags) that outperforms the state-of-the-art results by 1.9% on the development set and 1.0% on the test set. We also show that in intra-sentential code-switching, the selection of lexical items is constrained by POS categories, where function words tend to come more often from the dialectal language while the majority of content words come from the standard language.

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QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification
Younes Samih | Hamdy Mubarak | Ahmed Abdelali | Mohammed Attia | Mohamed Eldesouki | Kareem Darwish
Proceedings of the Fourth Arabic Natural Language Processing Workshop

This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification). In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks. These include deep neural nets and heuristics. Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps. Our system achieves F1-Scores of 66.1% and 67.0% on the development sets for Subtasks 1 and 2 respectively.

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Highly Effective Arabic Diacritization using Sequence to Sequence Modeling
Hamdy Mubarak | Ahmed Abdelali | Hassan Sajjad | Younes Samih | Kareem Darwish
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Arabic text is typically written without short vowels (or diacritics). However, their presence is required for properly verbalizing Arabic and is hence essential for applications such as text to speech. There are two types of diacritics, namely core-word diacritics and case-endings. Most previous works on automatic Arabic diacritic recovery rely on a large number of manually engineered features, particularly for case-endings. In this work, we present a unified character level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. Specifically, we employ a standard neural machine translation setup on overlapping windows of words (broken down into characters), and then we use voting to select the most likely diacritized form of a word. The proposed model outperforms all previous state-of-the-art systems. Our best settings achieve a word error rate (WER) of 4.49% compared to the state-of-the-art of 12.25% on a standard dataset.

2018

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Multi-Dialect Arabic POS Tagging: A CRF Approach
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali | Mohamed Eldesouki | Younes Samih | Randah Alharbi | Mohammed Attia | Walid Magdy | Laura Kallmeyer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Build Fast and Accurate Lemmatization for Arabic
Hamdy Mubarak
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM
Randah Alharbi | Walid Magdy | Kareem Darwish | Ahmed AbdelAli | Hamdy Mubarak
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Learning from Relatives: Unified Dialectal Arabic Segmentation
Younes Samih | Mohamed Eldesouki | Mohammed Attia | Kareem Darwish | Ahmed Abdelali | Hamdy Mubarak | Laura Kallmeyer
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Arabic dialects do not just share a common koiné, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.

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QCRI Live Speech Translation System
Fahim Dalvi | Yifan Zhang | Sameer Khurana | Nadir Durrani | Hassan Sajjad | Ahmed Abdelali | Hamdy Mubarak | Ahmed Ali | Stephan Vogel
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

This paper presents QCRI’s Arabic-to-English live speech translation system. It features modern web technologies to capture live audio, and broadcasts Arabic transcriptions and English translations simultaneously. Our Kaldi-based ASR system uses the Time Delay Neural Network (TDNN) architecture, while our Machine Translation (MT) system uses both phrase-based and neural frameworks. Although our neural MT system is slower than the phrase-based system, it produces significantly better translations and is memory efficient. The demo is available at https://st.qcri.org/demos/livetranslation.

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SemEval-2017 Task 3: Community Question Answering
Preslav Nakov | Doris Hoogeveen | Lluís Màrquez | Alessandro Moschitti | Hamdy Mubarak | Timothy Baldwin | Karin Verspoor
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe SemEval–2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016: (A) Question–Comment Similarity, (B) Question–Question Similarity, (C) Question–External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A–D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A–C.

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Arabic Diacritization: Stats, Rules, and Hacks
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali
Proceedings of the Third Arabic Natural Language Processing Workshop

In this paper, we present a new and fast state-of-the-art Arabic diacritizer that guesses the diacritics of words and then their case endings. We employ a Viterbi decoder at word-level with back-off to stem, morphological patterns, and transliteration and sequence labeling based diacritization of named entities. For case endings, we use Support Vector Machine (SVM) based ranking coupled with morphological patterns and linguistic rules to properly guess case endings. We achieve a low word level diacritization error of 3.29% and 12.77% without and with case endings respectively on a new multi-genre free of copyright test set. We are making the diacritizer available for free for research purposes.

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A Neural Architecture for Dialectal Arabic Segmentation
Younes Samih | Mohammed Attia | Mohamed Eldesouki | Ahmed Abdelali | Hamdy Mubarak | Laura Kallmeyer | Kareem Darwish
Proceedings of the Third Arabic Natural Language Processing Workshop

The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general. Segmentation of words into its constituent parts is an important processing building block. In this paper, we show how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources. We deal with segmentation as a sequence labeling problem at the character level. We show experimentally that our model can rival state-of-the-art methods that rely on additional resources.

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Arabic POS Tagging: Don’t Abandon Feature Engineering Just Yet
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali | Mohamed Eldesouki
Proceedings of the Third Arabic Natural Language Processing Workshop

This paper focuses on comparing between using Support Vector Machine based ranking (SVM-Rank) and Bidirectional Long-Short-Term-Memory (bi-LSTM) neural-network based sequence labeling in building a state-of-the-art Arabic part-of-speech tagging system. Using SVM-Rank leads to state-of-the-art results, but with a fair amount of feature engineering. Using bi-LSTM, particularly when combined with word embeddings, may lead to competitive POS-tagging results by automatically deducing latent linguistic features. However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements. We also show that gains that realized by using embeddings may not be additive with the gains achieved by the features. We are open-sourcing both the SVM-Rank and the bi-LSTM based systems for free.

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Abusive Language Detection on Arabic Social Media
Hamdy Mubarak | Kareem Darwish | Walid Magdy
Proceedings of the First Workshop on Abusive Language Online

In this paper, we present our work on detecting abusive language on Arabic social media. We extract a list of obscene words and hashtags using common patterns used in offensive and rude communications. We also classify Twitter users according to whether they use any of these words or not in their tweets. We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean). We make this dataset freely available for research, in addition to the list of obscene words and hashtags. We are also publicly releasing a large corpus of classified user comments that were deleted from a popular Arabic news site due to violations the site’s rules and guidelines.

2016

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Arabic to English Person Name Transliteration using Twitter
Hamdy Mubarak | Ahmed Abdelali
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Social media outlets are providing new opportunities for harvesting valuable resources. We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems. Such resources are crucial in translation and retrieval tasks. We demonstrate the benefits of the approach on Arabic to English transliteration. The contribution of this approach includes the size of data that can be collected and exploited within the span of a limited time; the approach is very generic and can be adopted to other languages and the ability of the approach to cope with new transliteration phenomena and trends. A statistical transliteration system built using this data improved a comparable system built from Wikipedia wikilinks data.

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Farasa: A New Fast and Accurate Arabic Word Segmenter
Kareem Darwish | Hamdy Mubarak
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we present Farasa (meaning insight in Arabic), which is a fast and accurate Arabic segmenter. Segmentation involves breaking Arabic words into their constituent clitics. Our approach is based on SVMrank using linear kernels. The features that we utilized account for: likelihood of stems, prefixes, suffixes, and their combination; presence in lexicons containing valid stems and named entities; and underlying stem templates. Farasa outperforms or equalizes state-of-the-art Arabic segmenters, namely QATARA and MADAMIRA. Meanwhile, Farasa is nearly one order of magnitude faster than QATARA and two orders of magnitude faster than MADAMIRA. The segmenter should be able to process one billion words in less than 5 hours. Farasa is written entirely in native Java, with no external dependencies, and is open-source.

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Farasa: A Fast and Furious Segmenter for Arabic
Ahmed Abdelali | Kareem Darwish | Nadir Durrani | Hamdy Mubarak
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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SemEval-2016 Task 3: Community Question Answering
Preslav Nakov | Lluís Màrquez | Alessandro Moschitti | Walid Magdy | Hamdy Mubarak | Abed Alhakim Freihat | Jim Glass | Bilal Randeree
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Classifying Arab Names Geographically
Hamdy Mubarak | Kareem Darwish
Proceedings of the Second Workshop on Arabic Natural Language Processing

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Best Practices for Crowdsourcing Dialectal Arabic Speech Transcription
Samantha Wray | Hamdy Mubarak | Ahmed Ali
Proceedings of the Second Workshop on Arabic Natural Language Processing

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QCRI@QALB-2015 Shared Task: Correction of Arabic Text for Native and Non-Native Speakers’ Errors
Hamdy Mubarak | Kareem Darwish | Ahmed Abdelali
Proceedings of the Second Workshop on Arabic Natural Language Processing

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Answer Selection in Arabic Community Question Answering: A Feature-Rich Approach
Yonatan Belinkov | Alberto Barrón-Cedeño | Hamdy Mubarak
Proceedings of the Second Workshop on Arabic Natural Language Processing

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QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English
Massimo Nicosia | Simone Filice | Alberto Barrón-Cedeño | Iman Saleh | Hamdy Mubarak | Wei Gao | Preslav Nakov | Giovanni Da San Martino | Alessandro Moschitti | Kareem Darwish | Lluís Màrquez | Shafiq Joty | Walid Magdy
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Using Twitter to Collect a Multi-Dialectal Corpus of Arabic
Hamdy Mubarak | Kareem Darwish
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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Automatic Correction of Arabic Text: a Cascaded Approach
Hamdy Mubarak | Kareem Darwish
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

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Verifiably Effective Arabic Dialect Identification
Kareem Darwish | Hassan Sajjad | Hamdy Mubarak
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging
Kareem Darwish | Ahmed Abdelali | Hamdy Mubarak
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents an end-to-end automatic processing system for Arabic. The system performs: correction of common spelling errors pertaining to different forms of alef, ta marbouta and ha, and alef maqsoura and ya; context sensitive word segmentation into underlying clitics, POS tagging, and gender and number tagging of nouns and adjectives. We introduce the use of stem templates as a feature to improve POS tagging by 0.5\% and to help ascertain the gender and number of nouns and adjectives. For gender and number tagging, we report accuracies that are significantly higher on previously unseen words compared to a state-of-the-art system.