Kareem Darwish


2021

pdf bib
QADI: Arabic Dialect Identification in the Wild
Ahmed Abdelali | Hamdy Mubarak | Younes Samih | Sabit Hassan | Kareem Darwish
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Proper dialect identification is important for a variety of Arabic NLP applications. In this paper, we present a method for rapidly constructing a tweet dataset containing a wide range of country-level Arabic dialects —covering 18 different countries in the Middle East and North Africa region. Our method relies on applying multiple filters to identify users who belong to different countries based on their account descriptions and to eliminate tweets that either write mainly in Modern Standard Arabic or mostly use vulgar language. The resultant dataset contains 540k tweets from 2,525 users who are evenly distributed across 18 Arab countries. Using intrinsic evaluation, we show that the labels of a set of randomly selected tweets are 91.5% accurate. For extrinsic evaluation, we are able to build effective country level dialect identification on tweets with a macro-averaged F1-score of 60.6% across 18 classes.

pdf bib
Arabic Offensive Language on Twitter: Analysis and Experiments
Hamdy Mubarak | Ammar Rashed | Kareem Darwish | Younes Samih | Ahmed Abdelali
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers useoffensive language. Lastly, we conduct many experiments to produce strong results (F1 =83.2) on the dataset using SOTA techniques.

pdf bib
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
Firoj Alam | Shaden Shaar | Fahim Dalvi | Hassan Sajjad | Alex Nikolov | Hamdy Mubarak | Giovanni Da San Martino | Ahmed Abdelali | Nadir Durrani | Kareem Darwish | Abdulaziz Al-Homaid | Wajdi Zaghouani | Tommaso Caselli | Gijs Danoe | Friso Stolk | Britt Bruntink | Preslav Nakov
Findings of the Association for Computational Linguistics: EMNLP 2021

With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.

pdf bib
A Few Topical Tweets are Enough for Effective User Stance Detection
Younes Samih | Kareem Darwish
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

User stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing stance detection on vocal Twitter users, who have many tweets on a target, can be highly accurate (+98%). However, such methods perform poorly or fail completely for less vocal users, who may have authored only a few tweets about a target. In this paper, we tackle stance detection for such users using two approaches. In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings, which capture latent meanings of words in context. We show that this approach outperforms two strong baselines and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, which may not be topically relevant, and then we perform unsupervised classification of the user, which entails clustering a user with other users in the training set. This approach achieves 95.6% accuracy and 93.1% macro F-measure.

pdf bib
ASAD: Arabic Social media Analytics and unDerstanding
Sabit Hassan | Hamdy Mubarak | Ahmed Abdelali | Kareem Darwish
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This system demonstration paper describes ASAD: Arabic Social media Analysis and unDerstanding, a suite of seven individual modules that allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. The suite is made available through a web API and a web interface where users can enter text or upload files.

2020

pdf bib
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

pdf bib
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.

pdf bib
Predicting the Topical Stance and Political Leaning of Media using Tweets
Peter Stefanov | Kareem Darwish | Atanas Atanasov | Preslav Nakov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a cascaded method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior; then, it uses supervised learning based on user labels to characterize both the general political leaning of online media and of popular Twitter users, as well as their stance with respect to the target polarizing topic. We evaluate the model by comparing its predictions to gold labels from the Media Bias/Fact Check website, achieving 82.6% accuracy.

pdf bib
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.

pdf bib
Bert Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets
Fouzi Harrag | Maria Dabbah | Kareem Darwish | Ahmed Abdelali
Proceedings of the Fifth Arabic Natural Language Processing Workshop

During the last two decades, we have progressively turned to the Internet and social media to find news, entertain conversations and share opinion. Recently, OpenAI has developed a machine learning system called GPT-2 for Generative Pre-trained Transformer-2, which can produce deepfake texts. It can generate blocks of text based on brief writing prompts that look like they were written by humans, facilitating the spread false or auto-generated text. In line with this progress, and in order to counteract potential dangers, several methods have been proposed for detecting text written by these language models. In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots. Our dataset is based on tweets from a previous work, which we have crawled and extended using the Twitter API. We used GPT2-Small-Arabic to generate fake Arabic Sentences. For evaluation, we compared different recurrent neural network (RNN) word embeddings based baseline models, namely: LSTM, BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new transfer-learning model has obtained an accuracy up to 98%. To the best of our knowledge, this work is the first study where ARABERT and GPT2 were combined to detect and classify the Arabic auto-generated texts.

pdf bib
Improving Arabic Text Categorization Using Transformer Training Diversification
Shammur Absar Chowdhury | Ahmed Abdelali | Kareem Darwish | Jung Soon-Gyo | Joni Salminen | Bernard J. Jansen
Proceedings of the Fifth Arabic Natural Language Processing Workshop

Automatic categorization of short texts, such as news headlines and social media posts, has many applications ranging from content analysis to recommendation systems. In this paper, we use such text categorization i.e., labeling the social media posts to categories like ‘sports’, ‘politics’, ‘human-rights’ among others, to showcase the efficacy of models across different sources and varieties of Arabic. In doing so, we show that diversifying the training data, whether by using diverse training data for the specific task (an increase of 21% macro F1) or using diverse data to pre-train a BERT model (26% macro F1), leads to overall improvements in classification effectiveness. In our work, we also introduce two new Arabic text categorization datasets, where the first is composed of social media posts from a popular Arabic news channel that cover Twitter, Facebook, and YouTube, and the second is composed of tweets from popular Arabic accounts. The posts in the former are nearly exclusively authored in modern standard Arabic (MSA), while the tweets in the latter contain both MSA and dialectal Arabic.

2019

pdf bib
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.

pdf bib
Tanbih: Get To Know What You Are Reading
Yifan Zhang | Giovanni Da San Martino | Alberto Barrón-Cedeño | Salvatore Romeo | Jisun An | Haewoon Kwak | Todor Staykovski | Israa Jaradat | Georgi Karadzhov | Ramy Baly | Kareem Darwish | James Glass | Preslav Nakov
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

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what’s behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.

pdf bib
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.

pdf bib
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.

pdf bib
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

pdf bib
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)

pdf bib
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

pdf bib
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.

pdf bib
Proceedings of the Third Arabic Natural Language Processing Workshop
Nizar Habash | Mona Diab | Kareem Darwish | Wassim El-Hajj | Hend Al-Khalifa | Houda Bouamor | Nadi Tomeh | Mahmoud El-Haj | Wajdi Zaghouani
Proceedings of the Third Arabic Natural Language Processing Workshop

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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

pdf bib
QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features
Mohamed Eldesouki | Fahim Dalvi | Hassan Sajjad | Kareem Darwish
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

The paper describes the QCRI submissions to the task of automatic Arabic dialect classification into 5 Arabic variants, namely Egyptian, Gulf, Levantine, North-African, and Modern Standard Arabic (MSA). The training data is relatively small and is automatically generated from an ASR system. To avoid over-fitting on such small data, we carefully selected and designed the features to capture the morphological essence of the different dialects. We submitted four runs to the Arabic sub-task. For all runs, we used a combined feature vector of character bi-grams, tri-grams, 4-grams, and 5-grams. We tried several machine-learning algorithms, namely Logistic Regression, Naive Bayes, Neural Networks, and Support Vector Machines (SVM) with linear and string kernels. However, our submitted runs used SVM with a linear kernel. In the closed submission, we got the best accuracy of 0.5136 and the third best weighted F1 score, with a difference less than 0.002 from the highest score.

pdf bib
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

pdf bib
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.

2015

pdf bib
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)

pdf bib
Randomized Greedy Inference for Joint Segmentation, POS Tagging and Dependency Parsing
Yuan Zhang | Chengtao Li | Regina Barzilay | Kareem Darwish
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Proceedings of the Second Workshop on Arabic Natural Language Processing
Nizar Habash | Stephan Vogel | Kareem Darwish
Proceedings of the Second Workshop on Arabic Natural Language Processing

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

pdf bib
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

2014

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

pdf bib
Simple Effective Microblog Named Entity Recognition: Arabic as an Example
Kareem Darwish | Wei Gao
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Despite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER. NER on microblogs presents many complications such as informality of language, shortened named entities, brevity of expressions, and inconsistent capitalization (for cased languages). We introduce simple effective language-independent approaches for improving NER on microblogs, based on using large gazetteers, domain adaptation, and a two-pass semi-supervised method. We use Arabic as an example language to compare the relative effectiveness of the approaches and when best to use them. We also present a new dataset for the task. Results of combining the proposed approaches show an improvement of 35.3 F-measure points over a baseline system trained on news data and an improvement of 19.9 F-measure points over the same system but trained on microblog data.

pdf bib
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.

pdf bib
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)

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

pdf bib
Arabizi Detection and Conversion to Arabic
Kareem Darwish
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

2013

pdf bib
Named Entity Recognition using Cross-lingual Resources: Arabic as an Example
Kareem Darwish
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Translating Dialectal Arabic to English
Hassan Sajjad | Kareem Darwish | Yonatan Belinkov
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

pdf bib
Subjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs
Ahmed Mourad | Kareem Darwish
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2012

pdf bib
Transliteration Mining Using Large Training and Test Sets
Ali El Kahki | Kareem Darwish | Mohamed Abdul-Wahab | Ahmed Taei
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Arabic Retrieval Revisited: Morphological Hole Filling
Kareem Darwish | Ahmed Ali
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

pdf bib
Improved Transliteration Mining Using Graph Reinforcement
Ali El Kahki | Kareem Darwish | Ahmed Saad El Din | Mohamed Abd El-Wahab | Ahmed Hefny | Waleed Ammar
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

pdf bib
Transliteration Mining with Phonetic Conflation and Iterative Training
Kareem Darwish
Proceedings of the 2010 Named Entities Workshop

pdf bib
Classifying Wikipedia Articles into NE’s Using SVM’s with Threshold Adjustment
Iman Saleh | Kareem Darwish | Aly Fahmy
Proceedings of the 2010 Named Entities Workshop

pdf bib
Simplified Feature Set for Arabic Named Entity Recognition
Ahmed Abdul-Hamid | Kareem Darwish
Proceedings of the 2010 Named Entities Workshop

2008

pdf bib
Automatic Extraction of Textual Elements from News Web Pages
Hossam Ibrahim | Kareem Darwish | Abdel-Rahim Madany
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we present an algorithm for automatic extraction of textual elements, namely titles and full text, associated with news stories in news web pages. We propose a supervised machine learning classification technique based on the use of a Support Vector Machine (SVM) classifier to extract the desired textual elements. The technique uses internal structural features of a webpage without relying on the Document Object Model to which many content authors fail to adhere. The classifier uses a set of features which rely on the length of text, the percentage of hypertext, etc. The resulting classifier is nearly perfect on previously unseen news pages from different sites. The proposed technique is successfully employed in Alzoa.com, which is the largest Arabic news aggregator on the web.

2007

pdf bib
Arabic Cross-Document Person Name Normalization
Walid Magdy | Kareem Darwish | Ossama Emam | Hany Hassan
Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources

pdf bib
BioNoculars: Extracting Protein-Protein Interactions from Biomedical Text
Amgad Madkour | Kareem Darwish | Hany Hassan | Ahmed Hassan | Ossama Emam
Biological, translational, and clinical language processing

2006

pdf bib
Building a Heterogeneous Information Retrieval Collection of Printed Arabic Documents
Abdelrahim Abdelsapor | Noha Adly | Kareem Darwish | Ossama Emam | Walid Magdy | Magdi Nagi
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes the development of an Arabic document image collection containing 34,651 documents from 1,378 different books and 25 topics with their relevance judgments. The books from which the collection is obtained are a part of a larger collection 75,000 books being scanned for archival and retrieval at the bibliotheca Alexandrina (BA). The documents in the collection vary widely in topics, fonts, and degradation levels. Initial baseline experiments were performed to examine the effectiveness of different index terms, with and without blind relevance feedback, on Arabic OCR degraded text.

pdf bib
Arabic OCR Error Correction Using Character Segment Correction, Language Modeling, and Shallow Morphology
Walid Magdy | Kareem Darwish
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

pdf bib
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
Kareem Darwish | Mona Diab | Nizar Habash
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages

pdf bib
Examining the Effect of Improved Context Sensitive Morphology on Arabic Information Retrieval
Kareem Darwish | Hany Hassan | Ossama Emam
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages

2002

pdf bib
Building a Shallow Arabic Morphological Analyser in One Day
Kareem Darwish
Proceedings of the ACL-02 Workshop on Computational Approaches to Semitic Languages