September 2023 - Corpora

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 19 Sep '23

19 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 19 Sep '23

19 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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2023 Law & Corpus Linguistics Conference Registration
by Brett Hashimoto 18 Sep '23

18 Sep '23

Hello! We're looking forward to the 8th annual Law and Corpus Linguistics Conference at Brigham Young University on Friday, October 13th. Information about the conference can be found HERE<https://corpusconference.byu.edu/2023-home/>. Please make note of the following information in preparation for the conference: Registration You can now register for the conference HERE<https://corpusconference.byu.edu/2023-home/registration/>. Please register early so we can make appropriate plans for meeting spaces and meals. There is no registration fee for this conference. Pre-conference Workshop There will be a workshop, titled Introduction to Corpus Linguistic Applications to Law, on Thursday from 9:00 - 12:00 for linguists who are interested in learning more about legal applications of corpus linguistics. The workshop is free of charge. If you are interested, please register HERE<https://corpusconference.byu.edu/2023-home/pre-conference/>. Lunch will be provided immediately following the workshop. Meals There will be a dinner for registered attendees on Thursday, October 12 at 6:00 pm. Please RSVP on the registration form if you plan to attend. Breakfast, lunch, and snacks will be provided on Friday. Location and accommodation The conference will be held in the J. Reuben Clark Law Building at Brigham Young University. We recommend the Provo Marriott Hotel or the Hyatt Place Provo for nearby accommodation. Travel scholarships We are offering $500 travel scholarships for law students, graduate students in linguistics, or early career professionals. If you are in one of these categories and need assistance with travel funding, please email byulawcorpus(a)law.byu.edu<mailto:byulawcorpus@law.byu.edu> with a brief letter describing how you will benefit from this scholarship and attendance at LCL 2023. Please don't hesitate to reach out if you have additional questions. We hope to see you in October! LCL 2023 Conference Organizing Team

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Call for Abstracts: Sustainable archiving, exploitation, distribution of social media data
by Tatjana Scheffler 18 Sep '23

18 Sep '23

CALL FOR ABSTRACTS "SUSTAINABLE ARCHIVING, EXPLOITATION, AND DISTRIBUTION OF DYNAMIC DATA FROM SOCIAL MEDIA - TWITTER AND BEYOND" Conference from 19 - 20 March 2024 at the German National Library (Frankfurt am Main) Website: https://www.dnb.de/EN/twittertagung Social media is both a source of data for and the focus of a range of research approaches in the humanities, social sciences, IT, sciences and life sciences. The development of social media over time makes it a part of our digital cultural heritage, but the process for institutions to archive and document these in ways which fully reflect its detail and complexity is still only rudimentary. One key reason for this is the unique characteristics of the data in terms of media technology, economics, social factors and aesthetics. This confronts researchers, research institutions and cultural heritage institutions with many different challenges in terms of how to archive, catalogue and provide the data for later use. One example of this is Twitter (now known as “X”). The monetisation of the platform’s internal archive (part of ongoing restructuring of the platform) has had a radical impact on research and archiving. While flexible APIs and access opportunities before early 2023 led to a boom in research activity and the creation of comprehensive collections, access for research and archive has been made increasingly difficult since then. Archiving, cataloguing and providing dynamic data from social media present challenges which affect researchers, research institutions, libraries and archives in equal measure, and the best way to solve these problems is through collaboration and partnership. This requires wide-ranging efforts which would be impossible for a single data community or discipline. The aim of the conference is to facilitate networking between libraries, archives, research institutes and researchers in German-speaking countries who are involved in archiving and long-term use of data and digital objects from social media. Conference presentations should focus on the following topics: • The interaction between research and archiving • Research data problems in Tweet-based research caused by the loss of Twitter as a data provider • The status and maintenance of social media from an archival and cultural-historical perspective, e.g. posts, interactions and platform elements • The consolidation of collections, corpora, and holdings such as metadata • Initiatives to encourage archiving and cataloguing of social media data • Concepts for providing derivative datasets from social media and how these can be used • Ethical questions • Legal issues • The possibility of creating a social media data registry Please submit your abstract (max. 1 page / 500 words) and no more than 1 page of biobibliographic data as a PDF in German or English. Up to 20 minutes are available for each presentation, plus 10 minutes for discussion. Submission to: twarchiv(a)dnb.de <mailto:twarchiv@dnb.de> Deadline for submission of abstracts: 31 October 2023 feedback on acceptance of paper: 30 November 2023 Conference The conference will take place from about noon, 19 March 2024 and end in the early evening of 20 March. It will be followed by a data sprint working with a long-term corpus of German Twitter data on 21 & 22 March 2024. More information coming soon. Conference: 19. - 20. March 2024 Data Sprint: 21. - 22. March 2023 Venue: German National Library, Frankfurt am Main Speakers who do not have their own resources for travel may apply for up to €300 to cover travel and accommodation costs. Organisation: Dr. Britta Woldering, Letitia Mölck, German National Library, twarchiv(a)dnb.de <mailto:twarchiv@dnb.de> Programme Committee (in alphabetical order) Stefan Dietze (Heinrich Heine University Düsseldorf) Dimitar Dimitrov (GESIS) Christoph Eggersglüß (Philipps University Marburg, NFDI4Culture) Philippe Genêt (German National Library, Text+) Tatjana Scheffler (Ruhr University Bochum) Claus-Michael Schlesinger (University Library, Humboldt University Berlin) Britta Woldering (German National Library) Cooperation partners: Deutsche Nationalbibliothek BERD@NFDI KonsortSWD NFDI4Culture NFDI4Data Science Text+ --- Jun.-Prof. Dr. Tatjana Scheffler (she/her) GB 5/157 Ruhr-Universität Bochum Fakultät für Philologie, Germanistik Universitätsstraße 150 44801 Bochum Germany Mail: tatjana.scheffler(a)rub.de Web: http://staff.germanistik.rub.de/digitale-forensische-linguistik/ Tel.: +49 234 32-21471

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Professor (W2 with Tenure Track to W3) for Speech Science
by Dietrich Klakow 18 Sep '23

18 Sep '23

Saarland University is a campus-based university with a strong international focus and a research-oriented profile. Numerous research institutes on campus and the systematic promotion of collaborative projects make Saarland University an ideal environment for innovation and technology transfer. To further strengthen this excellence in research and teaching, the Department of Language Science and Technology seeks to hire a Professor (W2 with Tenure Track to W3) for Speech Science (m|f|x) Reference n° W2283 Six-year tenure track position, starting April 2025, with the possibility of promotion to a permanent professorship (W3). We are looking for a highly motivated researcher in the field of phonetics, speech science, and speech technology, with extensive knowledge of speech production, perception and acoustics. The successful candidate is expected to have expertise in experimental and computational approaches to research on spoken language. A focus on spoken dialog and conversational speech and/or multimodal aspects of communication is particularly welcome. The Department of Language Science and Technology is internationally recognized for collaborative and interdisciplinary research, and the successful candidate is expected to contribute to relevant joint research initiatives. A demonstrated ability to attract external funding of research projects is therefore highly desired. Phonetics, speech science and speech technology are core elements of our study programs on the M.Sc. and B.Sc./B.A. level, and the successful candidate is expected to teach the associated courses within these programs. What we can offer you: Tenure track professors (W2) have faculty status at Saarland University, including the right to supervise Bachelor’s, Master’s and PhD students. The successful candidate will focus on carrying out world-class research, will lead their own research group, and will undertake teaching and supervision responsibilities. Tenure track professors (W2) with outstanding performance will receive tenure as a full professor (W3) provided a positive tenure evaluation is made. Decisions regarding tenure are made no later than six years after taking up the tenure track position. The position offers excellent working conditions in a lively and international scientific community. Saarland University is one of the leading centers for language science and computational linguistics in Europe, and offers a dynamic and stimulating research environment. The Department of Language Science and Technology organizes about 100 research staff in nine research groups in the fields of computational linguistics, psycholinguistics, phonetics and speech science, speech processing, and corpus linguistics (https://www.uni-saarland.de/en/department/lst.html). The department serves as the focal point of the Collaborative Research Center 1102 "Information Density and Linguistic Encoding" (http://www.sfb1102.uni-saarland.de). It is part of the Saarland Informatics Campus (https://saarland-informatics-campus.de/en), which brings together 800 researchers and 2,000 students from 81 countries and collaborates closely with world-class research institutions on campus, such as the Max Planck Institute for Informatics, the Max Planck Institute for Software Systems, and the German Research Center for Artificial Intelligence (DFKI). Qualifications: The appointment will be made in accordance with the general provisions of German public sector employment law. Applicants will have a PhD or doctorate in an appropriate subject and will have demonstrated a proven track record of independent academic research (e.g. as a junior or assistant professor, or by having completed an advanced, post-doctoral research degree (Habilitation) or equivalent academic activity at a university or research institution). They will typically have completed a period of postdoctoral research and have teaching experience at the university level. They must have demonstrated outstanding research capabilities and have the potential to successfully lead their own research group. The successful candidate will be expected to actively contribute to departmental research and teaching, including introductory lectures in phonetics and phonology, speech science, as well as more advanced lectures. The teaching language is English (in the MSc programs) and German (in the BSc/BA programs). We expect that the successful candidate has, or is willing to acquire within an appropriate period, sufficient proficiency to teach in both languages. Your Application: Applications should be submitted online at www.uni-saarland.de/berufungen. No additional paper copy is required. The application must contain: • a cover letter and curriculum vitae (including phone number and email address) • a full list of publications • a full list of third-party funding (own shares shown) • your proposed research plan (2-5 pages) • a teaching statement (1 page) • copies of your degree certificates • full-text copies of your 5 most important publications • a list of 3 academic references (including email addresses), at least one of whom must be a person who is outside the group of your current or former supervisors or colleagues. Applications must be received no later than 12 October 2023. Please include the job reference number W2283 when you apply. Please contact crocker(a)lst.uni-saarland.de if you have any questions. Saarland University regards internationalization as an institution-wide process spanning all aspects of university life and it therefore encourages applications that align with its internationalization strategy. Members of the university's professorial staff are therefore expected to engage in activities that promote and foster further internationalization. Special support will be provided for projects that continue with or expand on collaborative interactions within existing international cooperative networks, e.g. projects with partners in the European University Alliance Transform4Europe (www.transform4europe.eu) or the University of the Greater Region (www.uni-gr.eu). Saarland University is an equal opportunity employer. In accordance with its affirmative action policy, Saarland University is actively seeking to increase the proportion of women in this field. Qualified women candidates are therefore strongly encouraged to apply. Preferential consideration will be given to applications from disabled candidates of equal eligibility. When you submit a job application to Saarland University you will be transmitting personal data. Please refer to our privacy notice (https://www.uni-saarland.de/verwaltung/datenschutz/) for information on how we collect and process personal data in accordance with Art. 13 of the General Data Protection Regulation (GDPR). By submitting your application, you confirm that you have taken note of the information in the Saarland University privacy notice.

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Introducing LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking and Prompt Engineering
by Firoj Alam 18 Sep '23

18 Sep '23

Dear all, We are happy to announce the release of our LLMeBench framework. The framework is designed to accelerate and simplify evaluation and benchmarking of large language models. It is modular, language-agnostic and simple to extend. It currently supports interactions with LLMs through APIs. The framework also features zero- and few-shot learning settings. It will be open-sourced to encourage improvements and extensions from the community. The framework currently hosts recipes for a diverse set of Arabic NLP tasks using OpenAI’s GPT and BLOOMZ models. Specifically, it currently serves 31 unique NLP tasks (from word-level to sentence pairs tasks) with specific focus on Arabic tasks, using 53 publicly available datasets. It also comes equipped with 200 prompts for these setups. It has recipes for 12 languages including Arabic, Bangla, Bulgarian, Dutch, English, French, German, Italian, Polish, Russian, Spanish, Turkish, and more to come. We hope this will encourage experimentation with LLMs for multilingual studies content. We extend an invitation to the research community to participate and improve the framework. We are excited to hear all your feedback and suggestions and we thank you for your contribution. For further details please take a look at the repository and the paper below. Code: https://github.com/qcri/LLMeBench Paper: https://arxiv.org/pdf/2308.04945.pdf Regards Firoj ................ Firoj Alam, PhD http://sites.google.com/site/firojalam/

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2025

2024

2023

2022

Corpora September 2023