- Corpora - ELRA lists

DEADLINE EXTENSION: The 22nd International Workshop on Treebanks and Linguistic Theories (TLT 2024)
by Zinsmeister, Heike 22 Aug '24

22 Aug '24

[With apologies for cross-posting] ----------------------------- NEW DEADLINE: August 22nd, 2024 ----------------------------- We are excited to announce the 22nd International Workshop on Treebanks and Linguistic Theories (TLT 2024), which will bring together developers and users of linguistically annotated natural language corpora. The workshop is endorsed by ACL SIGPARSE and will be hosted by Universität Hamburg in Germany on December 5th-6th, 2024. ----------------------------- VENUE ----------------------------- TLT 2024 will take place at the guest house of Universität Hamburg. In order to support rich discussions and networking, TLT 2024 will primarily be an in-person event; we will, however, accommodate a limited number of live / synchronous remote presentations, prioritizing those with circumstances that prevent travel. Universität Hamburg and its guest house are conveniently located near the Dammtor train station / metro station Stephansplatz which are well-connected with many parts of the city and beyond, providing an easy commute for attendees. Hamburg is a vibrant city known for its rich maritime history as one of the leading cities in the medieval Hanseatic League, as well as its modern cultural diversity, including events at the world-famous Elbphilharmonie Concert Hall. The city is easily accessible by train or plane (Hamburg Airport (HAM); about 1 to 1.5 hours train ride: Bremen Airport (BRE) and Hannover Airport (HAJ)). ----------------------------- SUBMISSION INFORMATION ----------------------------- TLT addresses all aspects of treebank design, development, and use. As ‘treebanks’ we consider any pairing of natural language data (spoken, signed, or written) with annotations of linguistic structure at various levels of analysis, including, e.g., morpho-phonology, syntax, semantics, and discourse. Annotations can take any form (including trees or general graphs), but they should be encoded in a way that enables computational processing. Reflections on the design of linguistic annotations, methodology studies, resource announcements or updates, annotation or conversion tool development, or reports on treebank usage including probing the leakage of treebanks into large language models are but some examples of the types of papers we anticipate for TLT. Papers should describe original work; they should emphasize completed work rather than intended work, and should indicate clearly the state of completion of the reported results. Submissions will be judged on correctness, originality, technical strength, significance and relevance to the conference, and interest to the attendees. We invite paper submissions in two distinct tracks: * regular papers on substantial and original research, including empirical evaluation results, where appropriate; * short papers on smaller, focused contributions, work in progress, negative results, surveys, or opinion pieces. Submissions (in both tracks) may either be archival—in case of unpublished work—or non-archival, based on the wish of the authors. All archival papers accepted for presentation at the workshop will be included in the TLT 2024 proceedings volume, which will be part of the ACL Anthology. Non-archival papers must have been published or accepted for publication at another CL conference. Long papers may consist of up to 8 pages of content (excluding references and appendices). Short papers may consist of up to 4 pages of content (excluding references and appendices). Accepted papers will be given an additional page to address reviewer comments. All submissions should follow the two-column format and the ACL style guidelines. We strongly recommend the use of the LaTeX style files, OpenDocument, or Microsoft Word templates created for ACL: https://github.com/acl-org/acl-style-files Submissions will be reviewed double-blind, and all full and short papers must be anonymous, i.e. not reveal author(s) on the title page or through self-references. So e.g., “We previously showed (Smith, 2020) …”, should be avoided. Instead, use citations such as “Smith (2020) previously showed …. Papers must be submitted digitally, in PDF, and uploaded through the on-line conference system (link forthcoming). Submissions that violate these requirements will be rejected without review. ----------------------------- IMPORTANT DATES ----------------------------- * Long and short paper submission deadlines: August 22th, 2024 * Reviews Due: September 26th, 2024 * Notification of acceptance: October 6th, 2024 * Final version of papers due: November 6th, 2024 * TLT2024: December 5th-6th, 2024 in Hamburg ----------------------------- TLT2024 WORKSHOP CHAIRS ----------------------------- Daniel Dakota, Indiana University Sandra Kübler, Indiana University Heike Zinsmeister, Universität Hamburg ----------------------------- TLT2024 COMMUNICATION CHAIR ----------------------------- Sarah Jablotschkin, Universität Hamburg Contact: tlt2024.gw(a)uni-hamburg.de Website: https://www.korpuslab.uni-hamburg.de/en/tlt2024.html

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August 2024 Newsletter - LDC
by Penn LDC 22 Aug '24

22 Aug '24

In this newsletter: Fall 2024 LDC Data Scholarship program New publications: LORELEI Uyghur Incident Language Pack<https://catalog.ldc.upenn.edu/LDC2024T07> Ravnursson Faroese Speech and Transcripts<https://catalog.ldc.upenn.edu/LDC2024S09> ________________________________ Fall 2024 LDC Data Scholarship program Student applications for the Fall 2024 LDC Data Scholarship program are being accepted now through September 15, 2024. This program provides eligible students with no-cost access to LDC data. Students must complete an application consisting of a data use proposal and letter of support from their advisor. For application requirements and program rules, visit the LDC Data Scholarships page<https://www.ldc.upenn.edu/language-resources/data/data-scholarships>. ________________________________ New publications: LORELEI Uyghur Incident Language Pack<https://catalog.ldc.upenn.edu/LDC2024T07> was developed by LDC and is comprised of 28 million words of Uyghur monolingual text, 500,000 words of English monolingual text, 3.3 million words of parallel and comparable Uyghur-English text, and 200,000 words annotated for simple named entities and situation frames. It constitutes all of the text data, annotations, supplemental resources, and related software tools for the Uyghur language that were used in the DARPA LORELEI / LoReHLT 2016 Evaluation<https://www.nist.gov/itl/iad/mig/lorehlt-evaluations>. The LORELEI (Low Resource Languages for Emergent Incidents) program was concerned with building human language technology for low resource languages in the context of emergent situations. In the evaluation scenario, an unforeseen event triggered a need for humanitarian and logistical support in a region where the incident language had received little or no attention in NLP research. Evaluation participants provided NLP solutions, including information extraction and machine translation, with limited resources and limited development time. Data was collected from news, social network, weblog, newsgroup, discussion forum, and reference material. Named entity annotation identified entities to be detected by systems for scoring purposes. Situation frame analysis was designed to extract basic information about needs and relevant issues for planning a disaster response effort. 2024 members can access this corpus through their LDC accounts. Non-members may license this data for a fee. * Ravnursson Faroese Speech and Transcripts<https://catalog.ldc.upenn.edu/LDC2024S09> contains 109 hours of Faroese prompted speech from 433 speakers (249 female, 184 male), corresponding transcripts and speaker metadata. It is an extract from the Basic Language Resource Kit 1.0 (BLARK 1.0)<https://mtd.setur.fo/en/resource/ravnur-blark-1-0/> developed by the Faroe Islands' Ravnur Project<https://mtd.setur.fo/en/>. Speech data was collected in 2022. Speakers from all major dialect areas in the Faroe Islands in three age groups -- 15-35, 36-60, and 61+ years -- read texts that included a word list, a phrase list, closed vocabulary readings, and short texts. Recordings also contain spontaneous speech. Orthographic transcripts are included. 2024 members can access this corpus through their LDC accounts provided they have submitted a completed copy of the special license agreement. Non-members may license this data at no cost. To unsubscribe from this newsletter, log in to your LDC account<https://catalog.ldc.upenn.edu/login> and uncheck the box next to "Receive Newsletter" under Account Options or contact LDC for assistance. Membership Coordinator Linguistic Data Consortium<ldc.upenn.edu> University of Pennsylvania T: +1-215-573-1275 E: ldc(a)ldc.upenn.edu<mailto:ldc@ldc.upenn.edu> M: 3600 Market St. Suite 810 Philadelphia, PA 19104

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SICon 2024: Deadline extension and GenSICon Shared Task
by aina.garisoler＠telecom-paris.fr 22 Aug '24

22 Aug '24

(Apologies for cross-posting) NEW DEADLINE: August 29, 2024 ****** Second Call for Papers ****** SICon 2024: 2nd Workshop on Social Influence in Conversations Website: https://sites.google.com/view/sicon2024/home Twitter/X: @SIConWorkshop Paper Submission via Openreview: https://openreview.net/group?id=EMNLP/2024/Workshop/SiCon Contact: sicon-chairs [at] googlegroups.com Venue: Co-located with EMNLP 2024; November 16, 2024; Miami, Florida *** Workshop description *** Social influence (SI) is the change in an individual's thoughts, feelings, attitudes, or behaviors from interacting with another individual or a group. For example, a buyer uses SI skills to negotiate trade-offs and build rapport with the seller. SI is ubiquitous in everyday life, and hence, realistic human-machine conversations must reflect these dynamics, making it essential to model and understand SI in dialogue research systematically. This would improve SI systems' ability to understand users’ utterances, tailor communication strategies, personalize responses, and actively lead conversations. These challenges draw on perspectives not only from NLP and AI research but also from Game Theory, Affective Computing, Communication, and Social Psychology. SICon 2024 will be the second edition of a venue that uniquely fosters a dedicated discussion on social influence within NLP while involving researchers from other disciplines such as affective computing and the social sciences. SICon 2024 features keynote talks, panel discussions, poster sessions, and lightning talks for accepted papers. We encourage researchers of all stages and backgrounds to share their exciting work! SICon will promote discussion around several key questions: * How should social influence systems model users and plan optimal responses systematically? * How can social influence systems benefit from linguistic theories (e.g.,successful persuasion or negotiation tactics) developed in the social sciences? * What structurally differentiates and unites various social influence tasks? * What are the ethical issues involved with AI that engage in social influence and what guardrails must be implemented before using these systems in the wild? *** Submission Guidelines *** SICon welcomes two types of papers: regular workshop submissions and shared task submissions. - Regular workshop submissions Regular workshop submissions are archival short (4 pages) and long (8 pages) papers. There is also a non-archival track for extended abstracts (2 pages) covering ongoing work on social influence NLP. Topics include but are not limited to: Analysis-focused contributions (e.g., associations between linguistic behaviors or user attributes with SI task outcomes); System design contributions (e.g., dialogue systems for SI tasks such as strategic games, emotional support, etc.; SI systems effectively harnessing the capabilities of LLMs); Contributions advancing relevant subgoals in SI tasks (e.g. detecting SI strategies in text, partner/opponent modeling and emotion recognition in SI interactions); SI systems benefited from linguistic theories (e.g., successful persuasion, negotiation tactics) developed in social science; Ethical issues and guardrails involved with AI that engage in SI; Unintentional aspects of SI for any human-facing NLP system; Datasets capturing forms of SI; Opinion or position papers on SI. Submissions should follow the official EMNLP 2024 style guidelines and be submitted through OpenReview: https://openreview.net/group?id=EMNLP/2024/Workshop/SiCon - Shared task submissions The GenSICon shared task aims to investigate the generalization capability of a particular model across different social influence tasks or scenarios. Further details and a submission link can be found here: https://sites.google.com/view/sicon2024/shared-task We have prizes for the winners! *** Important Dates *** Direct paper submission deadline: August 29, 2024 Shared task paper submission deadline: September 27, 2024 Notification of acceptance: October 2, 2024 Camera-ready paper due: October 10, 2024 Workshop: November 16, 2024 (All submission deadlines are 11:59 p.m. UTC-12:00 ‘anywhere on Earth’) *** Contact *** For any questions, please contact the organizers at: sicon-chairs [at] googlegroups.com Consider joining our slack community! A dedicated space to connect researchers working on various topics related to social influence in NLP and beyond, as well as a useful communication channel for live announcements during the workshop. https://join.slack.com/t/acl2023sicon/shared_invite/zt-1y7cv1c2v-Lm21Sm6KX3… *** Organizers *** Muskan Garg (Mayo Clinic) Kushal Chawla (Capital One) Weiyan Shi (Stanford NLP) Ritam Dutt (Carnegie Mellon University) Deuksin Kwon (University of Southern California) James Hale (University of Southern California) Liang Qiu (Amazon) Aina Garí Soler (Télécom-Paris) Alexandros Papangelis (Amazon Alexa AI) Gale Lucas (University of Southern California) Zhou Yu (Columbia University) Daniel Hershcovich (University of Copenhagen)

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New Named Entity Corpus for Occupational Substance Exposure Assessment
by Paul Thompson 22 Aug '24

22 Aug '24

We are pleased to announce the release of a new annotated corpus, consisting of selected sections (i.e., Abstract, Methods and Results) of scientific research articles concerning occupational exposures to two different types of substance, i.e., diesel exhaust (51 articles) and respirable crystalline silica (50 articles). The article sections have been annotated by experts in the field with 6 categories of named entities relevant to the assessment of occupational substance exposures, particularly in the context of Job Exposure Matrices. The corpus and associated annotation guidelines may be downloaded from: https://zenodo.org/records/11164271 NER models and associated code are available at: https://github.com/panagiotis-geo/Substance_Exposure_NER/ The development of the corpus and the associated NER models are described in more detail in the following article: Thompson, P., Ananiadou, S., Basinas I., Brinchmann, B. C., Cramer, C., Galea, K. S., Ge, C., Georgiadis, P., Kirkeleit, J., Kuijpers, E., Nguyen, N., Nuñez, R., Schlünssen, V., Stokholm, Z. A., Taher, E. A., Tinnerberg, H., Van Tongeren, M. and Xie, Q. (2024). <https://doi.org/10.1371/journal.pone.0307844> Supporting the working life exposome: annotating occupational exposure for enhanced literature search. PLoS ONE 19(8): e030784 https://doi.org/10.1371/journal.pone.0307844 Abstract ————— An individual’s likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures. -- Paul Thompson Research Fellow Department of Computer Science National Centre for Text Mining Manchester Institute of Biotechnology University of Manchester 131 Princess Street Manchester M1 7DN UK Tel: 0161 306 3091 http://personalpages.manchester.ac.uk/staff/Paul.Thompson/

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Call for participation: The 34th Meeting on Computational Linguistics in the Netherlands, Leiden University
by Leiden University - CLIN34 22 Aug '24

22 Aug '24

Dear colleague, The 34th Meeting of Computational Linguistics in The Netherlands (CLIN34) will take place soon, on Friday 30 August 2024! We cordially invite you to participate. Online registration<https://clin34.leidenuniv.nl/2024/07/05/registration-open/> ends on Wednesday (21st of August). Besides a large and diverse programme of posters and oral presentations, we are happy to report that CLIN34 will have two keynote talks by: * Diana Maynard, Sheffield University * Dominique Blok and Erik de Graaf, TNO The programme can also be found at: clin34.leidenuniv.nl/program/<https://clin34.leidenuniv.nl/program/> We hope to see you in Leiden about two weeks from now! The CLIN34 organizers Leiden University

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Academic positions in the University of Cambridge
by alk23＠cam.ac.uk 22 Aug '24

22 Aug '24

The Centre for Human-Inspired Artificial Intelligence (CHIA) at the University of Cambridge is looking to appoint new academic staff: Two Assistant Professors and one Teaching Associate in Human-Inspired Artificial Intelligence. Successful candidates will have core technical expertise in machine learning, human-computer interaction, computer vision, speech and/or natural language processing, robotics, mobile systems or another area of human-centred or human-facing AI. In addition to excellence in their area, we emphasise a proven ability to work interdisciplinarily with other disciplines that are central to the development of human-inspired AI. We are also looking for commitment to CHIA's mission to develop AI that can contribute to social and global progress. For full details and how to apply for these positions, see Two Assistant Professors https://www.jobs.cam.ac.uk/job/47407/ Deadline: 1 September, 2024 One Teaching Associate https://www.jobs.cam.ac.uk/job/47788/ Deadline: 30 September, 2024

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Student contest: 2024 Babel Young Writers' Competition
by Tristan Miller 22 Aug '24

22 Aug '24

Dear all, Below is a call for submissions to our annual contest for student writers. Contributions from undergraduate students of computational linguistics are most welcome. Sincerely, Tristan Miller Babel Advisory Panel ---------------------- This year, Babel: The Language Magazine <https://babelzine.co.uk/> will be running the tenth edition of our Young Writers' Competition, which encourages young linguists who are starting out on their study of language. The competition is open to anyone studying a linguistics-related subject at the 16–18-year-old or undergraduate level. The winner(s) will have their article published in Babel's 50th issue (Spring 2025) and receive a year's subscription to the magazine. Keep an eye on @Babelzine on X or @babel_zine on Instagram for inspiration from previous winners on topics ranging from sign language to spoonerisms, and from language birth to language death. Competition rules are as follows: Topic: An original discussion of any linguistic topic, written in an accessible and interesting style Length: 2000 to 2500 words Deadline: Monday, 16 December 2024 Format: Word file Submission: By e-mail to babelthelanguagemagazine(a)gmail.com with the subject "Young Writer's Competition" Please e-mail babelthelanguagemagazine(a)gmail.com if you have any questions about the competition. -- Dr. Tristan Miller, Assistant Professor Department of Computer Science, University of Manitoba https://clam.logological.org/ | Tel. +1 204 474 6792

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[CfP] Diversity and Change in Easy German @ DGfS 2025
by lena.wieland＠uni-saarland.de 22 Aug '24

22 Aug '24

CfP: Diversity and Change in Easy German (Workshop at DGfS 2025) Date: March 5-7, 2025 Location: University of Mainz, Germany Meeting Email: workshop-easy-german-dgfs2025(a)uni-saarland.de<mailto:workshop-easy-german-dgfs2025@uni-saarland.de> Website: https://sfb1102.uni-saarland.de/vielfalt-und-wandel-in-leichter-sprache/ Linguistic Field(s): Applied Linguistics, Computational Linguistics, Psycholinguistics Language Family: Germanic Call Deadline: August 18, 2024 Shortened Workshop Description: Easy German, which has been systematically developed since the 2000s to aid individuals with learning difficulties among others, focuses on enhancing text comprehensibility by avoiding linguistic complexity. Despite its intended uniformity, there is a lack of consensus on its precise conceptualization, with various frameworks and guidelines proposing different approaches. This workshop aims to: 1. Provide a platform for researchers to discuss the production and evolution of Easy German texts. 2. Highlight dynamic changes and variability in Easy German texts compared to Standard German. 3. Examine the cognitive processing of Easy German through psycholinguistic studies involving the target demographic. 4. Critically assess AI-driven systems for Easy German text production, exploring their implications, opportunities, and challenges. For further information, please visit the workshop website: https://sfb1102.uni-saarland.de/vielfalt-und-wandel-in-leichter-sprache/ Organizers: Ingo Reich (Saarland University, Germany) Heike Zinsmeister (University of Hamburg, Germany) Sarah Jablotschkin (University of Hamburg, Germany) Lena Wieland (Saarland University, Germany) Invited Speakers: Bettina Bock (University of Cologne) Ted Sanders (Utrecht University) Call for Papers: We invite contributions on all aspects of Easy German and easy-to-read variants in other Germanic languages. The workshop will include a small poster session, and submissions for both talks and posters are welcome. Contributions in English are preferred, but submissions in German are also accepted. * Submission Details: * Abstract submission deadline: August 18, 2024 * Abstracts should be submitted to workshop-easy-german-dgfs2025(a)uni-saarland.de<mailto:workshop-easy-german-dgfs2025@uni-saarland.de> * Abstracts should not exceed one page (DIN A4, 2.5 cm margins, 12pt font) * Examples, graphics, or references may be included on a second page Important Workshop Information: The workshop is part of the 47th annual meeting of the German Linguistic Society (DGfS 2025) at Johannes Gutenberg University Mainz. Participants must register for the DGfS conference and pay the conference fee. For more information, visit http://dgfs.uni-mainz.de<http://dgfs.uni-mainz.de/>. Important Dates: Deadline for abstract submission: August 18, 2024 Notification of acceptance: September 2, 2024 Workshop dates: March 5-7, 2025 -- Lena Wieland SFB 1102, Project T1 – Information Density and Linguistic Encoding in “Leichte Sprache” Universität des Saarlandes Campus A2.2 Raum 3.12 D-66123 Saarbrücken T: +49 681 302 57543 www.uni-saarland.de/fakultaet-p/nds/team/wieland<https://www.uni-saarland.de/fakultaet-p/nds/team/wieland.html>

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Release: 𝐐𝐚𝐛𝐚𝐬 - 𝐚𝐧 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐋𝐞𝐱𝐢𝐜𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐃𝐚𝐭𝐚𝐛𝐚𝐬
by Mustafa Jarrar 22 Aug '24

22 Aug '24

We are very happy to release 𝐐𝐚𝐛𝐚𝐬 - 𝐚𝐧 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐋𝐞𝐱𝐢𝐜𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 Birzeit University’s SinaLab for Computational Linguistics and Artificial Intelligence <https://sina.birzeit.edu/> has officially launched Qabas <https://sina.birzeit.edu/qabas>, an open-source lexicographic database for Arabic, designed specifically for Natural Language Processing (NLP) applications. Qabas stands out by linking its lexical entries (lemmas) with lemmas from 110 different lexicons and numerous morphologically annotated corpora (around 2 million tokens), creating an extensive lexicographic graph. This project has been under development for over fourteen years. Lexicons have evolved from being primarily hard-copy resources for human use to having substantial significance in NLP applications. Although Arabic is a highly resourced language in terms of traditional lexicons, not enough attention is given to developing AI-oriented lexicographic databases. Additionally, none of the Arabic lexicons are available open-source, due to copyright restrictions imposed by their owners. As for Qabas, it is an open-source Arabic lexicon designed for NLP applications, and its novelty lies in its synthesis of many lexical resources. Each lexical entry (i.e., lemma) in Qabas is linked with equivalent lemmas in 110 other lexicons, and with 12 morphologically-annotated corpora (about 2M tokens); The philosophy of Qabas is to construct a large lexicographic data graph by linking existing Arabic lexicons and annotated corpora. Qabas stands as the largest Arabic lexicon, encompassing about 58K lemmas (45K nominal lemmas, 12.5K verbal lemmas, and 500 function word lemmas). Prof. Mustafa Jarrar, the project’s manager and main author, emphasized the importance of making Qabas freely available as an open-source resource, allowing everyone to access and use it for both commercial and non-commercial purposes. Prof. Jarrar hopes that researchers, companies, and software developers will leverage the lexicon’s data to develop innovative content and applications that benefit humanity. Prof. Talal Shahwan, President of Birzeit University, stated that despite the challenging conditions in Palestine, the university remains committed to excellence and to its mission towards knowledge. He emphasized that this achievement was made possible by the dedication of the university’s faculty and researchers. Qabas is publicly available online at: https://sina.birzeit.edu/qabas To download Qabas and find out more, see: https://sina.birzeit.edu/qabas/about Article: https://www.jarrar.info/publications/JH24.pdf We’d love your feedback: Facebook: https://www.facebook.com/watch?v=880418097306662 LinkedIn: https://www.facebook.com/watch?v=880418097306662 Best --Mustafa __________________________ Mustafa Jarrar, PhD Professor of Artificial Intelligence Chair, PhD Program in Computer Science Birzeit University, Palestine Page: http://www.jarrar.info SinaLab: https://sina.birzeit.edu

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Lecturer/Associate Professor in Natural Language Processing at University College London (UCL)
by Yilmaz, Emine 22 Aug '24

22 Aug '24

University College London (UCL) Department of Computer Science invites applications for a Lecturer/Associate Professor position in Natural Language Processing. Interested applicants can submit their applications until September 5th using this link<https://www.ucl.ac.uk/work-at-ucl/search-ucl-jobs/details?jobId=25979&jobTi…>. About UCL UCL’s Department of Computer Science (CS) is a top-ranked Computer Science Department in the UK. In the 2021 Research Excellence Framework (REF) evaluation, UCL Computer Science was ranked second in the UK for research power and first in England. London is a global hub for AI, where UCL plays a central role through close collaborations and joint PhD programmes with for example Meta and Google DeepMind. About the role University College London, Department of Computer Science is seeking a Lecturer (equivalent of Assistant Professor in the UK)/Associate Professor to join the Natural Language Processing Group. Successful candidates are expected to contribute to the teaching and research activities at the department. Expected duties and responsibilities include conducting research in the broader field of natural language processing, securing funding and engagement in the management of research projects, and dissemination of research through publications at top conferences/journals, talks and external engagements. About you Candidates should have a PhD (or equivalent qualification) or have held a previous postdoctoral position in natural language processing, information retrieval, machine learning, or a strongly related field. Candidates are expected to have a strong publication record in top conferences such as ACL, ICLR, NeurIPS, EMNLP, SIGIR. Experience in applying for research funding is not necessary, but highly desired. We also welcome applications from candidates with research experience from industry. Please contact Emine Yilmaz (emine.yilmaz(a)ucl.ac.uk<mailto:emine.yilmaz@ucl.ac.uk>) if you need any further information.

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