Ritesh Kumar


2022

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IIT Dhanbad @LT-EDI-ACL2022- Hope Speech Detection for Equality, Diversity, and Inclusion
Vishesh Gupta | Ritesh Kumar | Rajendra Pamula
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Hope is considered significant for the wellbeing,recuperation and restoration of humanlife by health professionals. Hope speech reflectsthe belief that one can discover pathwaysto their desired objectives and become rousedto utilise those pathways. Hope speech offerssupport, reassurance, suggestions, inspirationand insight. Hate speech is a prevalent practicethat society has to struggle with everyday.The freedom of speech and ease of anonymitygranted by social media has also resulted inincitement to hatred. In this paper, we workto identify and promote positive and supportivecontent on these platforms. We work withseveral machine learning models to classify socialmedia comments as hope speech or nonhopespeech in English. This paper portraysour work for the Shared Task on Hope SpeechDetection for Equality, Diversity, and Inclusionat LT-EDI-ACL 2022.

2021

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Multilingual Protest News Detection - Shared Task 1, CASE 2021
Ali Hürriyetoğlu | Osman Mutlu | Erdem Yörük | Farhana Ferdousi Liza | Ritesh Kumar | Shyam Ratan
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

Benchmarking state-of-the-art text classification and information extraction systems in multilingual, cross-lingual, few-shot, and zero-shot settings for socio-political event information collection is achieved in the scope of the shared task Socio-political and Crisis Events Detection at the workshop CASE @ ACL-IJCNLP 2021. Socio-political event data is utilized for national and international policy- and decision-making. Therefore, the reliability and validity of these datasets are of the utmost importance. We split the shared task into three parts to address the three aspects of data collection (Task 1), fine-grained semantic classification (Task 2), and evaluation (Task 3). Task 1, which is the focus of this report, is on multilingual protest news detection and comprises four subtasks that are document classification (subtask 1), sentence classification (subtask 2), event sentence coreference identification (subtask 3), and event extraction (subtask 4). All subtasks had English, Portuguese, and Spanish for both training and evaluation data. Data in Hindi language was available only for the evaluation of subtask 1. The majority of the submissions, which are 238 in total, are created using multi- and cross-lingual approaches. Best scores are above 77.27 F1-macro for subtask 1, above 85.32 F1-macro for subtask 2, above 84.23 CoNLL 2012 average score for subtask 3, and above 66.20 F1-macro for subtask 4 in all evaluation settings. The performance of the best system for subtask 4 is above 66.20 F1 for all available languages. Although there is still a significant room for improvement in cross-lingual and zero-shot settings, the best submissions for each evaluation scenario yield remarkable results. Monolingual models outperformed the multilingual models in a few evaluation scenarios.

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SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
Tiago Pimentel | Maria Ryskina | Sabrina J. Mielke | Shijie Wu | Eleanor Chodroff | Brian Leonard | Garrett Nicolai | Yustinus Ghanggo Ate | Salam Khalifa | Nizar Habash | Charbel El-Khaissi | Omer Goldman | Michael Gasser | William Lane | Matt Coler | Arturo Oncevay | Jaime Rafael Montoya Samame | Gema Celeste Silva Villegas | Adam Ek | Jean-Philippe Bernardy | Andrey Shcherbakov | Aziyana Bayyr-ool | Karina Sheifer | Sofya Ganieva | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Andrew Krizhanovsky | Natalia Krizhanovsky | Clara Vania | Sardana Ivanova | Aelita Salchak | Christopher Straughn | Zoey Liu | Jonathan North Washington | Duygu Ataman | Witold Kieraś | Marcin Woliński | Totok Suhardijanto | Niklas Stoehr | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Richard J. Hatcher | Emily Prud'hommeaux | Ritesh Kumar | Mans Hulden | Botond Barta | Dorina Lakatos | Gábor Szolnok | Judit Ács | Mohit Raj | David Yarowsky | Ryan Cotterell | Ben Ambridge | Ekaterina Vylomova
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, Võro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Asháninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems' predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving >90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems' performance on previously unseen lemmas.

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Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
Ekaterina Vylomova | Elizabeth Salesky | Sabrina Mielke | Gabriella Lapesa | Ritesh Kumar | Harald Hammarström | Ivan Vulić | Anna Korhonen | Roi Reichart | Edoardo Maria Ponti | Ryan Cotterell
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

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SIGTYP 2021 Shared Task: Robust Spoken Language Identification
Elizabeth Salesky | Badr M. Abdullah | Sabrina Mielke | Elena Klyachko | Oleg Serikov | Edoardo Maria Ponti | Ritesh Kumar | Ryan Cotterell | Ekaterina Vylomova
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource and endangered languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have different domains than desired application scenarios, demanding a need for domain and speaker-invariant language identification systems. This year’s shared task on robust spoken language identification sought to investigate just this scenario: systems were to be trained on largely single-speaker speech from one domain, but evaluated on data in other domains recorded from speakers under different recording circumstances, mimicking realistic low-resource scenarios. We see that domain and speaker mismatch proves very challenging for current methods which can perform above 95% accuracy in-domain, which domain adaptation can address to some degree, but that these conditions merit further investigation to make spoken language identification accessible in many scenarios.

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Anlirika: An LSTMCNN Flow Twister for Spoken Language Identification
Andreas Scherbakov | Liam Whittle | Ritesh Kumar | Siddharth Singh | Matthew Coleman | Ekaterina Vylomova
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

The paper presents Anlirika’s submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification. The task aims at building a robust system that generalizes well across different domains and speakers. The training data is limited to a single domain only with predominantly single speaker per language while the validation and test data samples are derived from diverse dataset and multiple speakers. We experiment with a neural system comprising a combination of dense, convolutional, and recurrent layers that are designed to perform better generalization and obtain speaker-invariant representations. We demonstrate that the task in its constrained form (without making use of external data or augmentation the train set with samples from the validation set) is still challenging. Our best system trained on the data augmented with validation samples achieves 29.9% accuracy on the test data.

2020

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Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
Ritesh Kumar | Atul Kr. Ojha | Bornini Lahiri | Marcos Zampieri | Shervin Malmasi | Vanessa Murdock | Daniel Kadar
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

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Evaluating Aggression Identification in Social Media
Ritesh Kumar | Atul Kr. Ojha | Shervin Malmasi | Marcos Zampieri
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

In this paper, we present the report and findings of the Shared Task on Aggression and Gendered Aggression Identification organised as part of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC - 2) at LREC 2020. The task consisted of two sub-tasks - aggression identification (sub-task A) and gendered identification (sub-task B) - in three languages - Bangla, Hindi and English. For this task, the participants were provided with a dataset of approximately 5,000 instances from YouTube comments in each language. For testing, approximately 1,000 instances were provided in each language for each sub-task. A total of 70 teams registered to participate in the task and 19 teams submitted their test runs. The best system obtained a weighted F-score of approximately 0.80 in sub-task A for all the three languages. While approximately 0.87 in sub-task B for all the three languages.

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Developing a Multilingual Annotated Corpus of Misogyny and Aggression
Shiladitya Bhattacharya | Siddharth Singh | Ritesh Kumar | Akanksha Bansal | Akash Bhagat | Yogesh Dawer | Bornini Lahiri | Atul Kr. Ojha
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

In this paper, we discuss the development of a multilingual annotated corpus of misogyny and aggression in Indian English, Hindi, and Indian Bangla as part of a project on studying and automatically identifying misogyny and communalism on social media (the ComMA Project). The dataset is collected from comments on YouTube videos and currently contains a total of over 20,000 comments. The comments are annotated at two levels - aggression (overtly aggressive, covertly aggressive, and non-aggressive) and misogyny (gendered and non-gendered). We describe the process of data collection, the tagset used for annotation, and issues and challenges faced during the process of annotation. Finally, we discuss the results of the baseline experiments conducted to develop a classifier for misogyny in the three languages.

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KMI-Panlingua-IITKGP @SIGTYP2020: Exploring rules and hybrid systems for automatic prediction of typological features
Ritesh Kumar | Deepak Alok | Akanksha Bansal | Bornini Lahiri | Atul Kr. Ojha
Proceedings of the Second Workshop on Computational Research in Linguistic Typology

This paper enumerates SigTyP 2020 Shared Task on the prediction of typological features as performed by the KMI-Panlingua-IITKGP team. The task entailed the prediction of missing values in a particular language, provided, the name of the language family, its genus, location (in terms of latitude and longitude coordinates and name of the country where it is spoken) and a set of feature-value pair are available. As part of fulfillment of the aforementioned task, the team submitted 3 kinds of system - 2 rule-based and one hybrid system. Of these 3, one rule-based system generated the best performance on the test set. All the systems were ‘constrained’ in the sense that no additional dataset or information, other than those provided by the organisers, was used for developing the systems.

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NUIG-Panlingua-KMI Hindi-Marathi MT Systems for Similar Language Translation Task @ WMT 2020
Atul Kr. Ojha | Priya Rani | Akanksha Bansal | Bharathi Raja Chakravarthi | Ritesh Kumar | John P. McCrae
Proceedings of the Fifth Conference on Machine Translation

NUIG-Panlingua-KMI submission to WMT 2020 seeks to push the state-of-the-art in Similar Language Translation Task for Hindi↔Marathi language pair. As part of these efforts, we conducteda series of experiments to address the challenges for translation between similar languages. Among the 4 MT systems prepared under this task, 1 PBSMT systems were prepared for Hindi↔Marathi each and 1 NMT systems were developed for Hindi↔Marathi using Byte PairEn-coding (BPE) into subwords. The results show that different architectures NMT could be an effective method for developing MT systems for closely related languages. Our Hindi-Marathi NMT system was ranked 8th among the 14 teams that participated and our Marathi-Hindi NMT system was ranked 8th among the 11 teams participated for the task.

2019

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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Sara Rosenthal | Noura Farra | Ritesh Kumar
Proceedings of the 13th International Workshop on Semantic Evaluation

We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.

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bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media
Ritesh Kumar | Guggilla Bhanodai | Rajendra Pamula | Maheswara Reddy Chennuru
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.

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Panlingua-KMI MT System for Similar Language Translation Task at WMT 2019
Atul Kr. Ojha | Ritesh Kumar | Akanksha Bansal | Priya Rani
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

The present paper enumerates the development of Panlingua-KMI Machine Translation (MT) systems for Hindi ↔ Nepali language pair, designed as part of the Similar Language Translation Task at the WMT 2019 Shared Task. The Panlingua-KMI team conducted a series of experiments to explore both the phrase-based statistical (PBSMT) and neural methods (NMT). Among the 11 MT systems prepared under this task, 6 PBSMT systems were prepared for Nepali-Hindi, 1 PBSMT for Hindi-Nepali and 2 NMT systems were developed for Nepali↔Hindi. The results show that PBSMT could be an effective method for developing MT systems for closely-related languages. Our Hindi-Nepali PBSMT system was ranked 2nd among the 13 systems submitted for the pair and our Nepali-Hindi PBSMTsystem was ranked 4th among the 12 systems submitted for the task.

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Predicting the Type and Target of Offensive Posts in Social Media
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Sara Rosenthal | Noura Farra | Ritesh Kumar
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)

As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.

2018

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Aggression-annotated Corpus of Hindi-English Code-mixed Data
Ritesh Kumar | Aishwarya N. Reganti | Akshit Bhatia | Tushar Maheshwari
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign
Marcos Zampieri | Shervin Malmasi | Preslav Nakov | Ahmed Ali | Suwon Shon | James Glass | Yves Scherrer | Tanja Samardžić | Nikola Ljubešić | Jörg Tiedemann | Chris van der Lee | Stefan Grondelaers | Nelleke Oostdijk | Dirk Speelman | Antal van den Bosch | Ritesh Kumar | Bornini Lahiri | Mayank Jain
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of the VarDial workshop, collocated with COLING’2018. This year, the campaign included five shared tasks, including two task re-runs – Arabic Dialect Identification (ADI) and German Dialect Identification (GDI) –, and three new tasks – Morphosyntactic Tagging of Tweets (MTT), Discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). A total of 24 teams submitted runs across the five shared tasks, and contributed 22 system description papers, which were included in the VarDial workshop proceedings and are referred to in this report.

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Part-of-Speech Annotation of English-Assamese code-mixed texts: Two Approaches
Ritesh Kumar | Manas Jyoti Bora
Proceedings of the First International Workshop on Language Cognition and Computational Models

In this paper, we discuss the development of a part-of-speech tagger for English-Assamese code-mixed texts. We provide a comparison of 2 approaches to annotating code-mixed data – a) annotation of the texts from the two languages using monolingual resources from each language and b) annotation of the text through a different resource created specifically for code-mixed data. We present a comparative study of the efforts required in each approach and the final performance of the system. Based on this, we argue that it might be a better approach to develop new technologies using code-mixed data instead of monolingual, ‘clean’ data, especially for those languages where we do not have significant tools and technologies available till now.

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Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
Ritesh Kumar | Atul Kr. Ojha | Marcos Zampieri | Shervin Malmasi
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

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Benchmarking Aggression Identification in Social Media
Ritesh Kumar | Atul Kr. Ojha | Shervin Malmasi | Marcos Zampieri
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. For this task, the participants were provided with a dataset of 15,000 aggression-annotated Facebook Posts and Comments each in Hindi (in both Roman and Devanagari script) and English for training and validation. For testing, two different sets - one from Facebook and another from a different social media - were provided. A total of 130 teams registered to participate in the task, 30 teams submitted their test runs, and finally 20 teams also sent their system description paper which are included in the TRAC workshop proceedings. The best system obtained a weighted F-score of 0.64 for both Hindi and English on the Facebook test sets, while the best scores on the surprise set were 0.60 and 0.50 for English and Hindi respectively. The results presented in this report depict how challenging the task is. The positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest in this topic.

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TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING’18
Ritesh Kumar | Guggilla Bhanodai | Rajendra Pamula | Maheshwar Reddy Chennuru
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards TRAC-1 Shared Task on Aggression Identification in Social Media for COLING 2018. In this paper we label aggression identification into three categories: Overtly Aggressive, Covertly Aggressive and Non-aggressive. We train a model to differentiate between these categories and then analyze the results in order to better understand how we can distinguish between them. We participated in two different tasks named as English (Facebook) task and English (Social Media) task. For English (Facebook) task System 05 was our best run (i.e. 0.3572) above the Random Baseline (i.e. 0.3535). For English (Social Media) task our system 02 got the value (i.e. 0.1960) below the Random Bseline (i.e. 0.3477). For all of our runs we used Long Short-Term Memory model. Overall, our performance is not satisfactory. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.

2014

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Developing Politeness Annotated Corpus of Hindi Blogs
Ritesh Kumar
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper I discuss the creation and annotation of a corpus of Hindi blogs. The corpus consists of a total of over 479,000 blog posts and blog comments. It is annotated with the information about the politeness level of each blog post and blog comment. The annotation is carried out using four levels of politeness ― neutral, appropriate, polite and impolite. For the annotation, three classifiers ― were trained and tested maximum entropy (MaxEnt), Support Vector Machines (SVM) and C4.5 - using around 30,000 manually annotated texts. Among these, C4.5 gave the best accuracy. It achieved an accuracy of around 78% which is within 2% of the human accuracy during annotation. Consequently this classifier is used to annotate the rest of the corpus

2012

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Developing a POS tagger for Magahi: A Comparative Study
Ritesh Kumar | Bornini Lahiri | Deepak Alok
Proceedings of the 10th Workshop on Asian Language Resources

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Challenges in the development of annotated corpora of computer-mediated communication in Indian Languages: A Case of Hindi
Ritesh Kumar
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The present paper describes an ongoing effort to compile and annotate a large corpus of computer-mediated communication (CMC) in Hindi. It describes the process of the compilation of the corpus, the basic structure of the corpus and the annotation of the corpus and the challenges faced in the creation of such a corpus. It also gives a description of the technologies developed for the processing of the data, addition of the metadata and annotation of the corpus. Since it is a corpus of written communication, it provides quite a distinctive challenge for the annotation process. Besides POS annotation, it will also be annotated at higher levels of representation. Once completely developed it will be a very useful resource of Hindi for research in the areas of linguistics, NLP and other social sciences research related to communication, particularly computer-mediated communication..Besides this the challenges discussed here and the way they are tackled could be taken as the model for developing the corpus of computer-mediated communication in other Indian languages. Furthermore the technologies developed for the construction of this corpus will also be made available publicly.
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