- Corpora - ELRA lists

Second call, TAL journal, regular issue 65-1
by Emmanuel Morin 21 Sep '23

21 Sep '23

2024 Volume 65-1 - regular issue Deadline for submission: on the fly until December 31, 2023 Editors : Maxime Amblard, Cécile Fabre, Emmanuel Morin et Sophie Rosset NEW Non-thematic issues of the Automatic Language Processing journal become "on the fly". Thus, each article in issue 65-1 will be evaluated as soon as it is submitted and will be published, subject to its acceptance, within an indicative period of six months after its submission. The call for volume 65-1 is thus open until December 31, 2023. TOPICS The journal Automatic Language Processing has an open call for papers. Submissions may concern theoretical and experimental contributions on all aspects of written, spoken, and signed language processing and computational linguistics, both theoretical and experimental, for example: - Computational models of language - Linguistic resources - Statistical learning and modeling - Intermodality and multimodality - Language multiplicity and diversity - Semantics and comprehension - Information access and text mining - Language production and processing/generation/synthesis - Evaluation - Explicability and reproducibility - NLP in interaction with other disciplines, digital humanities This list is indicative. On all topics, it is essential that the aspects related to natural language processing are emphasized. We also welcome position papers and survey papers. LANGUAGE Manuscripts may be submitted in English or French. THE JOURNAL TAL (http://www.atala.org/revuetal <http://www.atala.org/revuetal> - Traitement Automatique des Langues / Natural Language Processing) is an international journal published by ATALA (French Association for Natural Language Processing) since 1960 with the support of CNRS (National Centre for Scientific Research). TAL has an electronic mode of publication. IMPORTANT DATES Deadline for submission: on the fly until December 31, 2023 Notification to authors after first review: two months after submission Notification to authors after second review: two months after the first review Publication: two months after the second review FORMAT SUBMISSION Papers must be between 20 and 25 pages long, including references and appendices (with no possible derogation on the length). TAL performs double-blind review: it is thus necessary to anonymise the manuscript and the name of the pdf file and to avoid self references. Style sheets are available for download on the Web site of the journal (https://www.atala.org/content/instruction-authors-style-files-0 <https://www.atala.org/content/instruction-authors-style-files-0>). Authors who intend to submit a paper are encouraged to upload your contribution via the menu "Paper submission" (PDF format). To do so, you will need to have an account on the sciencesconf platform. To create an account, go to the site _http://www.sciencesconf.org <http://www.sciencesconf.org/> and click on "create account" next to the "Connect" button at the top of the page. To submit, come back to the page (soon available) http://tal-65-1.sciencesconf.org/ <http://tal-65-1.sciencesconf.org/>, connect to you account and upload your submission.

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Job - Tenure-track Assistant Professor: Computational Linguistics, Cognitive Science
by Cecilia Alm 21 Sep '23

21 Sep '23

DETAILED JOB DESCRIPTION: The Department of Psychology at the Rochester Institute of Technology (RIT; www.rit.edu/psychology<http://www.rit.edu/psychology>) invites candidates to apply for a tenure-track Assistant Professor position starting in August 2024. We are seeking an energetic and enthusiastic psychologist who will serve as an instructor, researcher, and mentor to students in our undergraduate (Psychology, Neuroscience) and graduate programs (Masters in Experimental Psychology, Ph.D. in Cognitive Science). We are particularly looking to build a cohort of faculty who can contribute to the interdisciplinary Ph.D. program in Cognitive Science and contribute to research, mentoring, and teaching using computational and laboratory methods. Candidates should have expertise in an area of Cognitive Science such as cognitive or behavioral neuroscience, AI, computational/psycho-linguistics, cognitive psychology, comparative psychology, or related areas. We are particularly interested in individuals whose area of research expertise expands the current expertise of the faculty. Candidates who can teach courses in natural language processing or computational modeling courses are especially encouraged to apply. The Department of Psychology at RIT serves a rapidly expanding student population at a technical university. The position requires a strong commitment to teaching and mentoring, active research and publication, and a strong potential to attract external funding. Teaching and research are priorities for faculty at RIT, and all faculty are expected to mentor students through advising, research and in-class experiences. The successful candidate will be able to teach courses in our undergraduate cognitive psychology track (Memory & Attention, Language & Thought, Decision Making, Judgement & Problem Solving), will be expected to teach research methods/statistics courses at the undergraduate and graduate level, and teach and mentor students in our graduate programs. In addition, candidates must be able to do research and work effectively within the department’s existing lab space. RIT provides many opportunities for collaborative research across the institute in many diverse disciplines such as AI, Digital Humanities, Human-Centered Computing, and Cybersecurity. We are seeking individuals who have the ability and interest in contributing to a community committed tostudent-centeredness; professional development and scholarship; integrity and ethics; respect, diversity and pluralism; innovation and flexibility; and teamwork and collaboration. Select to view links to RIT’s core values<http://www.rit.edu/academicaffairs/policiesmanual/p040>, honor code<http://www.rit.edu/academicaffairs/policiesmanual/p030>, and statement of diversity.<http://www.rit.edu/academicaffairs/policiesmanual/p050> THE COLLEGE/ DEPARTMENT: The Department of Psychology at RIT offers B.S., M.S. degrees, Advanced Certificates, minors, immersions, electives, and a new interdisciplinary Ph.D. degree program in Cognitive Science. The B.S. degree provides a general foundation in psychology with specialized training in one of five tracks: biopsychology, clinical psychology, cognitive psychology, social psychology, and developmental psychology. The M.S. degree is in Experimental Psychology, with an Advanced Certificate offered in Engineering Psychology. We offer accelerated BS/MS programs with AI, Sustainability, and Experimental Psychology. The Ph.D. degree is in Cognitive Science and the program is broadly interdisciplinary with several partner units across the university. We also offer joint B.S. degrees in Human Centered Computing and Neuroscience. The College of Liberal Arts is one of nine colleges within Rochester Institute of Technology. The College has over 150 faculty in 13 departments in the arts, humanities and social sciences. The College currently offers fourteen undergraduate degree programs and five Master degrees, serving over 800 students. THE UNIVERSITY: Founded in 1829, Rochester Institute of Technology is a diverse and collaborative community of engaged, socially conscious, and intellectually curious minds. Through creativity and innovation, and an intentional blending of technology, the arts and design, we provide exceptional individuals with a wide range of academic opportunities, including a leading research program and an internationally recognized education for deaf and hard-of-hearing students. Beyond our main campus in Rochester, New York, RIT has international campuses in China, Croatia, Dubai, and Kosovo. And with more than 19,000 students and more than 125,000 graduates from all 50 states and over 100 nations, RIT is driving progress in industries and communities around the world. Find out more at www.rit.edu<http://www.rit.edu/> . REQUIRED MINIMUM QUALIFICATIONS: * Have PhD., or PhD. expected by July 1, 2024 in cognitive psychology or cognitive science related specialty (e.g. linguistics); * Have demonstrated ability to conduct independent research in psychology or closely related fields; * Have consistently and recently published; * Have demonstrated teaching ability and have taught college courses independently beyond TA; * Have demonstrated ability to supervise student research; * Demonstrate external research grant attainment potential; * Demonstrate expertise in research and teaching in cognitive science; * Show a career trajectory that emphasizes a balance between teaching and research; * Show a fit with the Department of Psychology’s general mission, teaching, research, and resources. * Ability to contribute in meaningful ways to the college’s continuing commitment to cultural diversity, pluralism, and individual differences. HOW TO APPLY: Apply online at http://careers.rit.edu/faculty; search openings, then Keyword Search 8262BR. Please submit your application, curriculum vitae, cover letter addressing the listed qualifications and upload the following attachments: • A brief teaching philosophy • A research statement that includes information about previous grant work, the potential for future grants, and information about one-on-one supervision of student research • The names, addresses and phone numbers for three references • Contribution to Diversity Statement<https://www.rit.edu/academicaffairs/facultyrecruitment/forms/Diversity_Stat…> You can contact the chair of the search committee, Caroline DeLong, Ph.D. with questions on the position at: cmdgsh(a)rit.edu<mailto:cmdgsh@rit.edu>. Review of applications will begin October 1, 2023 and will continue until an acceptable candidate is found. RIT does not discriminate. RIT is an equal opportunity employer that promotes and values diversity, pluralism, and inclusion. For more information or inquiries, please visit RIT/TitleIX<https://www.rit.edu/academicaffairs/policiesmanual/c270> or the U.S. Department of Education at ED.Gov<https://www2.ed.gov/about/offices/list/ocr/know.html#:~:text=OCR%20enforces….> Cecilia O. Alm, Ph.D. She/Her Joint Program Director, MS in AI, School of Information - rit.edu/study/artificial-intelligence-ms Director, AWARE-AI NSF Research Traineeship Program - rit.edu/nrtai/ Associate Director, Center for Human-aware AI - rit.edu/chai/ Director, Computational Linguistics and Speech Processing lab - rit.edu/clasp Professor, Department of Psychology, CLA, Rochester Institute of Technology Affiliated with the School of Information, Department of Computer Science, Ph.D. Program in Computing and Information Sciences, MS Program in Data Science 92 Lomb Memorial Drive Rochester NY 14623 rit.edu/directory/coagla-cecilia-alm W: 585-475-7327 M: 585-536-7539 coagla(a)rit.edu<mailto:coagla@rit.edu> [RIT | Rochester Institute of Technology]<https://www.rit.edu/> [Twitter]<https://twitter.com/AWAREAINRT> [LinkedIn] <https://www.linkedin.com/in/ceciliaoalm/> CONFIDENTIALITY NOTICE: The contents of this email message and any attachments are intended solely for the addressee(s) and may contain confidential and/or privileged information and may be legally protected from disclosure. If you are not the intended recipient of this message or their agent, or if this message has been addressed to you in error, please immediately alert the sender by reply email and then delete this message and any attachments. If you are not the intended recipient, you are hereby notified that any use, dissemination, copying, or storage of this message or its attachments is strictly prohibited.

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PhD Positions !!!
by Roshni Chakraborty 21 Sep '23

21 Sep '23

Dear All, We are looking for PhD and Postdoctoral applicants for our Computational Social Science Group (https://css.cs.ut.ee/) ! So, if you are interested, please write to us and if you know someone who might be interested, please spread the word!! What is the topic about? ==================== We are interested in topics related to misinformation, media biasness, fairness and explainability on online social media. We are also open to other topics if they overlap with the interests of our group. Why join us? ==================== You'll be part of Estonia's esteemed University of Tartu Institute of Computer Science (https://cs.ut.ee/en) which is in the modern Delta Centre (https://delta.ut.ee/en/), a beacon of technological innovation and research excellence. Scholarship/Salary ==================== For PhD applicants, the gross salary is 2000 Euros per month for four years, and for Postdocs, it will be 2500 Euros gross per month. We will support research related travels and there is no tuition fees for PhD students. Education Qualification: ==================== For PhD applicants, master's degree is required and for postdoc, PhD degree is required. We are open to discuss positions with non-computer science students (who have good programming knowledge). Please drop a mail for any query or to show your interests at contact(a)css.cs.ut.ee<mailto:contact@css.cs.ut.ee> or roshni.chakraborty(a)ut.ee<mailto:roshni.chakraborty@ut.ee> or rajesh.sharma(a)ut.ee<mailto:rajesh.sharma@ut.ee> ! Kind Regards, Roshni Chakraborty, Assistant Professor, Institute of Computer Science University of Tartu, Estonia

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Post-Doc Position in Quantitative Typology in Paris
by Sylvain Loiseau 20 Sep '23

20 Sep '23

The Autogramm research project https://autogramm.github.io/ is excited to announce an open position for a postdoctoral researcher in the field of Quantitative Linguistic Typology. This one-year contract comes with the potential for an additional year upon review. Funded by the ANR (the French National Research Agency), Autogramm focuses on exploring the power of dependency-annotated treebanks to elucidate the structures of under-represented and typologically diverse languages. Our project also explores the extraction of typological generalizations from treebanks using token-based (Levshina 2019) or typometric (Gerdes, Kahane, and Chen 2021) approaches. The appointed postdoctoral researcher will be affiliated with the Lacito Laboratory (Paris/Villejuif) and Lisn Laboratory (Université Paris-Saclay). The researcher will have the opportunity to develop a research project revolving around the creation of a typological database. This database will integrate data from treebanks (based on the Universal Dependency project) and Interlinear Glossed Text (IGT) of various languages. Our aim is to capture the full spectrum of linguistic diversity and generate nuanced, quantitative typological generalizations based on annotated structures, which could eventually lead to automated comparative grammars. Ideal candidates will possess a strong background in language typology and linguistic theory, along with a proven track record in corpus creation and quantitative corpus analysis. The postdoctoral position is set to commence between October 2023 and, at the latest, January 1st 2024 and will continue for two years. To apply, please compile your application into a single PDF file and email it at your earliest convenience to Sylvain Kahane (sylvain(a)kahane.fr) Kim Gerdes (gerdes(a)lisn.fr) and Sylvain Loiseau (sylvain.loiseau(a)univ-paris13.fr). We look forward to receiving your applications. ----- Sylvain Loiseau sylvain.loiseau(a)univ-paris13.fr Université Sorbonne Paris Nord 99 avenue Jean-Baptiste Clément F-93430 Villetaneuse Laboratoire « Langues et civilisations à tradition orale » (UMR 7107 CNRS) Campus CNRS 7, rue Guy Môquet (bât. D) F-94801 Villejuif Cedex http://lacito.cnrs.fr

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Postdoc position, Uppsala Unviersity
by Sara Stymne 20 Sep '23

20 Sep '23

Hi all, We are happy to announce a postdoc position in a cross-disciplinary project about natural hazards, climate policy, and sustainable development. The postdoc will be placed at the Department of Government at Uppsala University, but it has a broad profile, and candidates with a PhD in computational Linguistics and an interest in political science are welcome to apply. The content of the teaching duties is open to negotiation. For the full announcement see: https://www.jobb.uu.se/details/?positionId=657481 Application deadline: October 12 Best, Sara Stymne N?r du har kontakt med oss p? Uppsala universitet med e-post s? inneb?r det att vi behandlar dina personuppgifter. F?r att l?sa mer om hur vi g?r det kan du l?sa h?r: http://www.uu.se/om-uu/dataskydd-personuppgifter/ E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy

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Lemon and Noncognitive morphology
by Hugh Paterson III 19 Sep '23

19 Sep '23

Greetings, Does anyone know of any descriptions or approaches to using Ontolex/lemon with non-concatenative morphology? Is the assumption that Cv1Cv2C shaped words will have their own entries for each instance of changes for v1 and v2? If this is the case, then this radically increases the number of items in a dictionary when compared with languages with affix type morphology. Any pointers appreciated, Kind regards, Hugh

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Call for papers - AAC/CFP Corpus 26 - 2025
by Luca Pallanti 19 Sep '23

19 Sep '23

AAC/CFP Corpus 26 - 2025 - https://journals.openedition.org/corpus/ <https://journals.openedition.org/corpus/> Background noise or added value? Managing noise during computer processing of linguistic corpora Elisa Gugliotta, Luca Pallanti, Olivier Kraif, Iris Fabry et Martina Barletta (eds.) -------FRENCH VERSION BELOW----- The increasing influence of NLP-related methodologies on corpus linguistics has compelled researchers to reassess their practices for managing noise and its impact on research results (Fuchs & Habert, 2004; Léon, 2018; Zalmout et al., 2018). Whether working with long-diachronic corpora (e.g., medieval French), dialectal corpora with limited resources (e.g., oral or written texts in dialectal Arabic, cf. Arabizi), or corpora of texts deviating from the norm (e.g., learner corpora), conducting noise analysis becomes an essential step in drawing linguistic conclusions from the available data (Molinelli & Putzu, 2015; Scaglione, 2018; Litosseliti, 2018). This special issue of Corpus builds upon a workshop held in April 2023 (https://je-bruit-corpus.sciencesconf.org/) and offers an opportunity to examine noise management methods in the fields of NLP and corpus linguistics, as well as their impact on the quality of linguistic data (Kraif & Ponton, 2007; Goutte et al., 2012; Zeroual, 2018). The fundamental inquiries in any linguistic study revolve around defining the research object, understanding the nature of the data, and determining ways to preserve its inherent characteristics throughout the various processing steps (such as lemmatisation, normalisation, labelling, etc.) (Sarrica et al., 2016). Hence, selecting appropriate methods for identifying and controlling noise becomes crucial throughout the entire process, from data collection to the archiving phase, and from data preparation to annotation (Egbert & Baker, 2019). The definition of noise itself is diverse and far from self-evident. In the field of NLP alone, this term encompasses a wide range of highly heterogeneous phenomena, including web peritexts - such as hyperlinks, menus and computer codes - as well as code switching and instances of spelling or grammatical errors that punctuate productions (Al Sharou et al., 2021). This special issue aims to delve into the definition of noise, from a linguistic perspective, and the practices employed by researchers to mitigate the biases that can arise from it. These practices are implemented during collection, recording, and annotation of data. The question of noise inevitably emerges at each stage of the empirical process involved in data construction and analysis: 1. Noise during data collection and recording If one accepts the postulate that "linguistic data is a result" (Benveniste, 1966), decoding the noise stemming from data collection and recording becomes crucial. Depending on the research object, various factors may contribute to data alteration, including the researcher's preconceptions or the biases introduced by an OCR system (Jentsch & Porada, 2020). The key challenge lies in predicting or identifying the potential biases induced by these factors during the selection and formatting of data. This enables better control over subsequent research stages and ensures greater accuracy in the analysis process. 2. Data preparation and pre-processing The methods employed to refine raw data and prepare it for advanced manipulation can give rise to a significant source of noise (or, conversely, of silence, if noise elimination filters are applied). This is particularly evident during the data normalization process (Al Sharou et al., 2021). When transcribing data or correcting errors, researchers must make choices that inevitably influence the nature of the data, either by reducing or enriching its content. As a result, it becomes essential to anticipate the consequences of the transformations introduced by data processing methods (Tanguy, 2012). 3. The annotation process and metadata Initially, corpus annotation aims to enrich the data by categorizing units through a labelling process, depending on the developed analysis model (Péry-Woodley et al., 2011). However, while this process has the potential to introduce noise, it can result in detrimental silence (when missing or erroneous labels lead to incomplete results during data analysis or querying). The concept of metadata also raises questions: does categorizing data transform it into something different? Furthermore, does the absence of agreement or low agreement in annotations produced by humans reflect inter-individual variations akin to noise, or does it stem from the inherent vagueness of the categorizations themselves? *** At each and every step of the process, key methodological questions arise: what threshold can be considered acceptable for noise? How can we differentiate between noise and methodological bias? Is it possible to estimate noise without a ground truth? Which statistical tools are specific to corpus studies and enable the definition of confidence intervals? How can we strike a balance to prevent the noise resulting from compromising research outcomes? *** Proposals for articles may address these topics from a general point of view, offering a theoretical and methodological perspective. Alternatively, they can be based on one or more case studies that focus on specific observations, while highlighting the noise management methods employed throughout the study. References Al Sharou, K., Li, Z., & Specia, L. (2021). Towards a Better Understanding of Noise in Natural Language Processing. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), 5362. https://aclanthology.org/2021.ranlp-1.7 Benveniste, É. (1966). Problèmes de linguistique générale. Gallimard. Egbert, J., & Baker, P. (Eds.). (2019). Using corpus methods to triangulate linguistic analysis. Routledge. Fuchs, C., & Habert, B. (2004). Le traitement automatique des langues : Des modèles aux ressources. Le Français Moderne - Revue de linguistique Française, CILF (conseil international de la langue française), LXXII: 1, online. Goutte, C., Carpuat, M., & Foster, G. (2012). The impact of sentence alignment errors on phrase-based machine translation performance. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers. Jentsch, P., & Porada, S. (2020). From Text to Data : Digitization, Text Analysis and Corpus Linguistics. In S. Schwandt (Éd.), Digital Humanities Research (1re éd., Vol. 1, p. 89128). transcript Verlag / Bielefeld University Press. https://doi.org/10.14361/9783839454190-004 Kraif, O., & Ponton, C. (2007). Du bruit, du silence et des ambiguïtés : Que faire du TAL pour l'apprentissage des langues ? TALN 2007, 143152. https://hal.archives-ouvertes.fr/hal-01073706 Léon, J. (2018). Tal et linguistique : Application, expérimentation, instrumentalisation. ELA. Etudes de linguistique appliquee, 2(190), 195203. Litosseliti, L. (Ed.). (2018). Research methods in linguistics. Bloomsbury Publishing. Molinelli, P., & Putzu, I. (2015). Modelli epistemologici, metodologie della ricerca e qualità del dato. Dalla linguistica storica alla sociolinguistica storica. Franco Angeli. Péry-Woodley, M.-P., Afantenos, S. D., Ho-Dac, L.-M., & Asher, N. (2011). La ressource ANNODIS, un corpus enrichi d'annotations discursives. TAL, 52(3), 71101. Sarrica, M., Mingo, I., Mazzara, B., & Leone, G. (2016). The effects of lemmatization on textual analysis conducted with IRaMuTeQ: results in comparison. JADT2016: 13ème Journées Internacionales d'Analyse Statistique de Données Textuelles. Scaglione, F. (2018). "Lavorare"; il dato linguistico: Prospettive e limiti. Alcune considerazioni dall'esperienza dell'Atlante Linguistico della Sicilia (ALS). In G. Sampino (Éd.), Atti del convegno internazionale dei dottorandi (p. 101122). Tanguy, L. (2012). Complexification des données et des techniques en linguistique : contribution du TAL aux solutions et aux problèmes. HDR dissertation, Université de Toulouse 2 - le Mirail. Zalmout, N., Erdmann, A., & Habash, N. (2018). Noise-robust morphological disambiguation for dialectal Arabic. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 953-964). Zeroual, I. (2018). Building Arabic Corpora: Concepts, Methodologies, Tools, and Experiments (Doctoral dissertation, University of Maryland, USA). Retro-planning * July 2023: call for publications. * 10 November 2023: pre-selection based on article summaries. * March 2024: article submission deadline. * June 2024: response to the authors. * June-October 2024: review process with authors to submit the final version of the article. * November-December 2024: editing process. * January 2025: publication. Please note that this retro-planning outlines a general timeline and may vary depending on the specific publication requirements. Abstract submission * Your abstract should be no longer than 1,500 words, including bibliographical references. * Please submit your abstracts by November 10, 2023 to elisa.gugliotta(a)ilc.cnr.it and luca.pallanti(a)univ-lyon2.fr. ----- FRENCH VERSION------ Bruit de fond ou valeur ajoutée ? Gérer le bruit lors des traitements informatiques des corpus linguistiques Sous la direction de Elisa Gugliotta, Luca Pallanti, Olivier Kraif, Iris Fabry et Martina Barletta English version below L'influence croissante des méthodologies liées au TAL sur la linguistique de corpus oblige les chercheurs à réinterroger les pratiques de gestion du bruit et son impact dans les résultats de recherche (Fuchs & Habert, 2004 ; Léon, 2018 ; Zalmout et al., 2018). Qu'il s'agisse de corpus en diachronie longue (ex. français médiéval), de corpus dialectaux aux ressources limitées (ex. textes oraux ou écrits en arabe dialectal, cf. arabizi), ou encore de corpus de textes éloignés de la norme (ex. corpus d'apprenants), l'analyse du bruit est une étape nécessaire pour tirer des conclusions linguistiques des données ainsi évaluées (Molinelli & Putzu, 2015 ; Scaglione, 2018 ; Litosseliti, 2018). Ce numéro thématique de la revue Corpus, qui fait suite à une journée d'étude sur le même thème organisée en avril 2023 (https://je-bruit-corpus.sciencesconf.org/), sera l'occasion de réfléchir sur les méthodes de gestion du bruit dans les domaines du TAL et de la linguistique de corpus outillée, et à son impact sur la qualité des données linguistiques (Kraif et Ponton, 2007 ; Goutte et al., 2012 ; Zeroual, 2018). Les questions sous-jacentes à toute étude linguistique concernent la définition de l'objet de recherche, la nature des données elles-mêmes, et la manière de préserver autant que possible leurs caractéristiques dans les différents traitements (lemmatisation, normalisation, étiquetage, etc.) (Sarrica et al., 2016). Ainsi, le choix des méthodes d'identification et de contrôle du bruit, de la phase de collecte à celle d'archivage, de la préparation des données à l'annotation, joue un rôle fondamental (Egbert & Baker, 2019). La définition même du bruit est multiple, et ne va pas de soi : dans le seul champ du TAL, ce terme, souvent peu interrogé, désigne des phénomènes variables et très hétérogènes, allant des péritextes du Web - hyperliens, menus et codes informatiques - au code switching, en passant par les erreurs d'orthographe ou de grammaire qui émaillent les productions (Al Sharou et al., 2021). Ce numéro thématique propose de mener une réflexion sur la définition du bruit, dans une perspective linguistique, et sur les pratiques des chercheurs visant à réduire la portée des biais qui en découlent, que ce soit durant la collecte, l'enregistrement ou l'annotation des données. Dans le concret de la recherche, la question du bruit se pose à chaque étape de la démarche empirique de construction et d'analyse des données : 1. Le bruit pendant la collecte et l'enregistrement des données Si l'on accepte le postulat selon lequel " la donnée linguistique est un résultat " (Benveniste, 1966), comment décoder le bruit causé par le recueil des données et leur enregistrement ? En effet, en fonction des objets de recherche, il existe des facteurs potentiels d'altération des données, comme par exemple les préconceptions du chercheur, ou les biais introduits par un système OCR donné (Jentsch & Porada, 2020). L'enjeu consiste alors à prédire ou à déterminer les biais potentiels induits par ces facteurs lors de la sélection et la mise en forme des données pour mieux contrôler les phases de recherche successives. 2. La préparation et le prétraitement des données. Les méthodes choisies pour affiner les données brutes et les rendre disponibles pour des manipulations avancées peuvent représenter une importante source de bruit (ou, au contraire, de silence si on applique un filtre pour éliminer le bruit) : c'est notamment le cas du processus de normalisation des données (Al Sharou et al., 2021). Qu'il s'agisse de transcrire des données ou de corriger des erreurs, le chercheur fait des choix qui impactent nécessairement la nature des données, soit en les réduisant, soit en les enrichissant. Il s'agit donc d'anticiper les conséquences des transformations produites par les méthodes de traitement des données (Tanguy, 2012). 3. Le processus d'annotation et les métadonnées À la base, l'annotation des corpus est une étape visant l'enrichissement des données : en fonction du modèle d'analyse mis au point, le chercheur tente de catégoriser des unités à travers un processus d'étiquetage (Péry-Woodley et al., 2011). Cependant, si d'un côté ce processus peut générer du bruit, de l'autre, il peut être une cause de silence fort préjudiciable aux résultats des recherches et à leur interprétation (des étiquettes absentes ou erronées pouvant générer des résultats lacunaires lors de l'analyse ou du requêtage des données). La notion de métadonnée peut également être mise en cause : catégoriser une donnée signifie-t-il la transformer en quelque chose d'autre ? Par ailleurs, l'absence d'accord ou un faible accord dans les annotations produites par l'humain manifeste-t-il des variations interindividuelles assimilables à du bruit, ou au caractère trop vague des catégorisations en jeu ? *** A chaque étape se posent des questions méthodologiques centrales : à partir de quel seuil peut-on considérer le bruit comme acceptable ? Comment différencier bruit et biais méthodologique ? Comment estimer le bruit sans vérité de terrain ? Quels outils statistiques spécifiques à l'étude des corpus permettent de délimiter des intervalles de confiance ? Comment atteindre l'équilibre nécessaire pour que le bruit causé par les traitements des données ne compromette pas les résultats des recherches ? *** Les propositions d'article pourront aborder ces questions d'un point de vue général, sous un angle théorique et méthodologique, ou s'appuyer sur une ou plusieurs études de cas portant sur des observations particulières, en prenant soin de mettre en lumière les méthodes de gestion du bruit tout au long de l'étude. Retro-planning * Juillet 2023 : publication du l'Appel * 10 novembre 2023 : pré-sélection sur résumé * Mars 2024 : remise des articles. Juin 2024 : réponse aux auteurs * Juin-octobre 2024 : navette avec les auteurs pour remise de l'article en forme définitive. * Novembre-décembre 2024 : édition. * Janvier 2025 : publication. Soumission des résumés * Votre résumé comptera 1.500 mots au maximum, références bibliographiques inclues. * Merci de soumettre vos résumés pour le 10 novembre 2023 aux adresses elisa.gugliotta(a)ilc.cnr.it et luca.pallanti(a)univ-lyon2.fr Références Al Sharou, K., Li, Z., & Specia, L. (2021). Towards a Better Understanding of Noise in Natural Language Processing. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), 5362. https://aclanthology.org/2021.ranlp-1.7 Benveniste, É. (1966). Problèmes de linguistique générale. Gallimard. Egbert, J., & Baker, P. (Eds.). (2019). Using corpus methods to triangulate linguistic analysis. Routledge. Fuchs, C., & Habert, B. (2004). Le traitement automatique des langues : Des modèles aux ressources. Le Français Moderne - Revue de linguistique Française, CILF (conseil international de la langue française), LXXII: 1, online. Goutte, C., Carpuat, M., & Foster, G. (2012). The impact of sentence alignment errors on phrase-based machine translation performance. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers. Jentsch, P., & Porada, S. (2020). From Text to Data : Digitization, Text Analysis and Corpus Linguistics. In S. Schwandt (Éd.), Digital Humanities Research (1re éd., Vol. 1, p. 89128). transcript Verlag / Bielefeld University Press. https://doi.org/10.14361/9783839454190-004 Kraif, O., & Ponton, C. (2007). Du bruit, du silence et des ambiguïtés : Que faire du TAL pour l'apprentissage des langues ? TALN 2007, 143152. https://hal.archives-ouvertes.fr/hal-01073706 Léon, J. (2018). Tal et linguistique : Application, expérimentation, instrumentalisation. ELA. Etudes de linguistique appliquee, 2(190), 195203. Litosseliti, L. (Ed.). (2018). Research methods in linguistics. Bloomsbury Publishing. Molinelli, P., & Putzu, I. (2015). Modelli epistemologici, metodologie della ricerca e qualità del dato. Dalla linguistica storica alla sociolinguistica storica. Franco Angeli. Péry-Woodley, M.-P., Afantenos, S. D., Ho-Dac, L.-M., & Asher, N. (2011). La ressource ANNODIS, un corpus enrichi d'annotations discursives. TAL, 52(3), 71101. Sarrica, M., Mingo, I., Mazzara, B., & Leone, G. (2016). The effects of lemmatization on textual analysis conducted with IRaMuTeQ: results in comparison. JADT2016: 13ème Journées Internacionales d'Analyse Statistique de Données Textuelles. Scaglione, F. (2018). "Lavorare"; il dato linguistico: Prospettive e limiti. Alcune considerazioni dall'esperienza dell'Atlante Linguistico della Sicilia (ALS). In G. Sampino (Éd.), Atti del convegno internazionale dei dottorandi (p. 101122). Tanguy, L. (2012). Complexification des données et des techniques en linguistique : contribution du TAL aux solutions et aux problèmes. HDR dissertation, Université de Toulouse 2 - le Mirail. Zalmout, N., Erdmann, A., & Habash, N. (2018). Noise-robust morphological disambiguation for dialectal Arabic. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 953-964). Zeroual, I. (2018). Building Arabic Corpora: Concepts, Methodologies, Tools, and Experiments (Doctoral dissertation, University of Maryland, USA).

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Call for Participation: SemRel SemEval Shared Task 1
by Nedjma OUSIDHOUM 19 Sep '23

19 Sep '23

Dear corpora-list members, We are glad to announce the first SemEval shared task on Semantic Textual Relatedness (STR): A shared task on automatically detecting the degree of semantic relatedness (closeness in meaning) between pairs of sentences. The semantic relatedness of two language units has long been considered fundamental to understanding meaning (Halliday and Hasan, 1976; Miller and Charles, 1991), and automatically determining relatedness has many applications such as evaluating sentence representation methods, question answering, and summarization. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation. On the other hand, relatedness is a much broader concept that accounts for all the commonalities between two sentences: whether they are on the same topic, express the same view, originate from the same time period, one elaborates on (or follows from) the other, etc. For instance, for the following sentence pairs: - Pair 1: a. There was a lemon tree next to the house. b. The boy enjoyed reading under the lemon tree. - Pair 2: a. There was a lemon tree next to the house. b. The boy was an excellent football player. Most people will agree that the sentences in pair 1 are more related than the sentences in pair 2. In this task, new textual datasets will be provided for Afrikaans <https://en.wikipedia.org/wiki/Afrikaans>, Algerian Arabic <https://en.wikipedia.org/wiki/Algerian_Arabic>, Amharic <https://en.wikipedia.org/wiki/Amharic>, English, Hausa <https://en.wikipedia.org/wiki/Hausa_language>, Hindi <https://en.wikipedia.org/wiki/Hindi>, Indonesian <https://en.wikipedia.org/wiki/Indonesian_language>, Kinyarwanda <https://en.wikipedia.org/wiki/Kinyarwanda>, Marathi <https://en.wikipedia.org/wiki/Marathi_language>, Moroccan Arabic <https://en.wikipedia.org/wiki/Moroccan_Arabic>, Modern Standard Arabic <https://en.wikipedia.org/wiki/Modern_Standard_Arabic>, Punjabi <https://en.wikipedia.org/wiki/Punjabi_language>, Spanish <https://en.wikipedia.org/wiki/Spanish_language>, and Telugu <https://en.wikipedia.org/wiki/Telugu_language>. Data Each instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper: https://aclanthology.org/2023.eacl-main.55.pdf Tracks Each team can provide submissions for one, two or all of the tracks shown below: Track A: Supervised Participants are to submit systems that have been trained using the labeled training datasets provided. Participating teams are allowed to use any publicly available datasets (e.g., other relatedness and similarity datasets or datasets in any other languages). However, they must report additional data they used, and ideally report how impactful each resource was on the final results. Track B: Unsupervised Participants are to submit systems that have been developed without the use of any labeled datasets pertaining to semantic relatedness or semantic similarity between units of text more than two words long in any language. The use of unigram or bigram relatedness datasets (from any language) is permitted. Track C: Cross-lingual Participants are to submit systems that have been developed without the use of any labeled semantic similarity or semantic relatedness datasets in the target language and with the use of labeled dataset(s) from at least one other language. Note: Using labeled data from another track is mandatory for submission to this track. Deciding which track a submission should go to: - If a submission uses labeled data in the target language: submit to Track A - If a submission does not use labeled data in the target language but uses labeled data from another language: submit to Track C - If a submission does not use labeled data in any language: submit to Track B ** Here ‘labeled data’ refers to labeled datasets pertaining to semantic relatedness or semantic similarity between units of text more than two words long. Evaluation The official evaluation metric for this task is the Spearman rank correlation coefficient, which captures how well the system-predicted rankings of test instances align with human judgments. You can find the evaluation script for this shared task on our Github page <https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEva…> . Helpful Links - Competition Website: https://codalab.lisn.upsaclay.fr/competitions/15704 - Task Website: <https://afrisenti-semeval.github.io/> https://semantic-textual-relatedness.github.io - Twitter X: <https://twitter.com/AfriSenti2023> https://twitter.com/SemRel2024 - Contact organisers semrel-semeval-organisers(a)googlegroups.com - Google group for participants semrel-semeval-participants(a)googlegroups.com Important Dates - Training data ready: 11 September 2023 - Evaluation Starts: 10 January 2024 - Evaluation End: 31 January 2024 - System Description Paper Due: February 2024 - SemEval workshop: Summer 2024 - (co-located with a major NLP conference) References - Shima Asaadi, Saif Mohammad, Svetlana Kiritchenko. 2019. Big BiRD: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. - M. A. K. Halliday and R. Hasan. 1976. Cohesion in English. London: Longman. - George A Miller and Walter G Charles. 1991. Contextual Correlates of Semantic Similarity. Language and Cognitive Processes, 6(1):1–28 - Mohamed Abdalla, Krishnapriya Vishnubhotla, and Saif Mohammad. 2023. What Makes Sentences Semantically Related? A Textual Relatedness Dataset and Empirical Study. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 782–796, Dubrovnik, Croatia. Association for Computational Linguistics. Task Organizers Nedjma Ousidhoum Shamsuddeen Hassan Muhammad Mohamed Abdalla Krishnapriya Vishnubhotla Vladimir Araujo Meriem Beloucif Idris Abdulmumin Seid Muhie Yimam Nirmal Surange Christine De Kock Sanchit Ahuja Oumaima Hourrane Manish Shrivastava Alham Fikri Aji Thamar Solorio Saif M. Mohammad

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VLSP 2023 Evaluation Campaign on Vietnamese text and speech processing
by Nguyen Thi Minh Huyen 18 Sep '23

18 Sep '23

Dear all, I would like to share the information about the VLSP 2023 evaluation campaign on Vietnamese text and speech processing: https://vlsp.org.vn/vlsp2023/eval Registration is still open for several tasks. Thank you for your attention. Best regards, Huyen -- Nguyen Thi Minh Huyen Faculty of Mathematics, Mechanics and Informatics VNU University of Science 334 Nguyễn Trãi, Thanh Xuân, Hà Nội Tel: +84-24-38581530

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2 TT faculty openings at UT Austin
by Matt Lease 18 Sep '23

18 Sep '23

Assistant Professor, Social Informatics: https://apply.interfolio.com/132375 Assistant Professor, Health Informatics: https://apply.interfolio.com/132381 Please direct any questions to facultysearch(a)ischool.utexas.edu -- Matt Lease Professor School of Information University of Texas at Austin Voice: (512) 471-9350 · Fax: (512) 471-3971 · Office: UTA 5.536 http://www.ischool.utexas.edu/~ml

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2024

2023

2022