October 2022 - Corpora

CfP: HCI 2023 Session: "Semantic, artificial and computational interaction studies: Towards a behavioromics of multimodal communication"
by Andy Lücking 27 Oct '22

27 Oct '22

--- *Apologies for cross-posting* Call for papers HCI International 2023 Session: "Semantic, artificial and computational interaction studies: Towards a behavioromics of multimodal communication" Manual gestures, facial expressions, head movements, shrugs, laughter, body orientation, speech, pauses: they all contribute to constituting what is called "multimodal interaction". Aiming at natural (for humans) interfaces, the field of HCI paid attention to this social fact early on. It is also a vital topic in conversation analysis and cognitive science and begins to percolate to theoretical linguistics and (formal) semantics. Simultaneously, due to the digital turn, work on multimodal communication is expanded by data analytics, that is, statistical means to describe the form of communication. However, while conjoint in investigating a common empirical domain, there is little exchange between these fields. This session aims at bringing these branches together. Potential goals are to delineate experimental studies, computational methods, resource building, and exploration to integrate symbolic, statistical, laboratory, field, and corpus-based approaches -- a joint methodological endeavor that might be called "behavioromics." The session is open, but not restrcted, to topics such as the following: - dialogue semantics and dialogue systems - (big) gesture data - multimodal interaction data - creation and exploitation of multimodal corpora - virtual reality avatars as experimental setting - cross-modal tracking - data-based multimodal analysis - detecting multimodal ensembles or gestalts - automatic annotation of everything beyond written text - representation schemes for multimodal communication We want to emphasize that conceptual contributions are highly welcome! The conference session aims at providing a platform for bringing together semanticists, computer scientists and researchers from related fields that deal with multimodal interaction. We all work on virtually the same topic but from different angles, but there are way to few opportunities to get in touch. But exchange and seeing what others are doing is crucial to approach the above-outlined, methodological, empirical and theoretical challenges. The conference session will take place *virtually* in conjunction with HCI International 2023 (https://2023.hci.international/). Full papers will be published as part of the conference proceedings by Springer. *If you want to contribute, please send a message to Andy Lücking (luecking(a)em.uni-frankfurt.de).* Important dates: - December 04, 2022: upload abstract - Notification of review outcome: 20 December 2022 - February 14, 2023: full paper is due - 23-28 July 2023: HCI International conference (virtual) Session organizers: Cornelia Ebert (https://www.linguistik-in-frankfurt.de/personal/cornelia-ebert/) Andy Lücking (https://www.texttechnologylab.org/team/andy-luecking/; http://www.llf.cnrs.fr/en/Gens/L%C3%BCcking) Alexander Mehler (https://www.texttechnologylab.org/team/alexander-mehler/)

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Second call for paper-MMLow-ICON 2022 -reg
by Bharathi Raja Asoka Chakravarthi 27 Oct '22

27 Oct '22

Second Call for papers *WORKSHOP ON MULTIMODAL MACHINE LEARNING IN LOW-RESOURCE LANGUAGES at ICON 2022 <https://lcs2.in/ICON-2022/>* *Link: *https://sites.google.com/view/mmlow-icon2022/home?authuser=0 In recent years, the exploitation of the potential of big data has resulted in significant advancements in a variety of Computer Vision and Natural Language Processing applications. However, the majority of tasks addressed thus far have been primarily visual in nature due to the unbalanced availability of labelled samples across modalities (e.g., there are numerous large labelled datasets for images but few for audio or IMU-based classification), resulting in a large performance gap when algorithms are trained separately. With its origins in audio-visual speech recognition and, more recently, in language and vision projects such as image and video captioning, multimodal machine learning is a thriving multidisciplinary research field that addresses several of artificial intelligence's (AI) original goals by integrating and modelling multiple communicative modalities, including linguistic, acoustic, and visual messages. Due to the variability of the data and the frequently observed dependency between modalities, this study subject presents some particular problems for machine learning researchers. Because the majority of this hateful content is in regional languages, they easily slip past online surveillance algorithms that are designed to target articles written in resource-rich languages like English. As a result, low-resource regional languages in Asia, Africa, Europe, and South America face a shortage of tools, benchmark datasets, and machine learning approaches. This workshop aims to bring together members of the machine learning and multimodal data fusion fields in regional languages. We anticipate contributions that hate speech and emotional analysis in multimodality include video, audio, text, drawings, and synthetic material in regional language. This workshop's objective is to advance scientific study in the broad field of multimodal interaction, techniques, and systems, emphasising important trends and difficulties in regional languages, with a goal of developing a roadmap for future research and commercial success. We invite submissions on topics that include, but are not limited to, the following: - Multimodal Sentiment Analysis in regional languages - Hate content video detection in regional languages - Trolling and Offensive post detection in Memes - Multimodal data fusion and data representation for hate speech detection in regional language - Multimodal hate speech benchmark datasets and evaluations in regional languages - Multimodal fake news in regional languages - Data collection and annotation methodologies for safer social media in low-resourced languages - Content moderation strategies in regional languages - Cybersecurity and social media in regional languages *Important Dates:* *Paper Submission Deadline: * Oct 30, 2022 *Paper Acceptance Notification: *Nov 15, 2022 *Camera-ready Submission Deadline: * Dec 01, 2022 *Workshop*: Dec 15, 2022 with regards, Dr. Bharathi Raja Chakravarthi, Assistant Professor / Lecturer-above-the-bar School of Computer Science, University of Galway, Ireland Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland E-mail: bharathiraja.akr(a)gmail.com , bharathiraja.asokachakravarthi(a)universityofgalway.ie Google Scholar: https://scholar.google.com/citations?user=irCl028AAAAJ&hl=en Special Issue on Language Technology for Safer Online Social Media Platforms in Low-resource Eurasian Languages <https://dl.acm.org/pb-assets/static_journal_pages/tallip/pdf/TALLIP-SI-Lang…>

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Job: Experienced Researcher (R2) - Analysis of social media and disinformation- deadline 15 November 2022
by Irina Temnikova 26 Oct '22

26 Oct '22

Experienced Researcher (R2) - Analysis of social media and disinformation With respect to its intensive development in 2022, the Institute GATE (in Sofia, Bulgaria) is looking for: Experienced Researcher (R2) – Analysis of social media and disinformation Application deadline: 15 November 2022 GATE is a joint initiative between Sofia University “St. Kliment Ohridski”, Chalmers University of Technology, Sweden and Chalmers Industrial Technology, Sweden. The Swedish institutions are leading organizations in strategic initiatives such as AI Sweden, AI Research Centre, AI Innovation of Sweden and the Digital Twin Cities Centre. GATE develops research capacity and potential in Big Data and Artificial Intelligence, cultivating the next generation of leading scientists by expanding the existing research network and establishing long-term agreements with leading global organizations. The Institute builds sustainable stakeholder relationships, focusing on technological collaboration between government, industry, academia and non-governmental organizations towards Artificial Intelligence and smart decision-making models. We use artificial intelligence for a better and safer life. Be part of the future and participate in applied research that develops innovations in the field of big data and artificial intelligence in collaboration with the scientific community, business and government. For our research team, we are looking for young at heart, motivated for high results and open to the unlimited possibilities of the future. Experienced Researcher Research group: Analysis of social media and disinformation Disinformation is not a new phenomenon; however, it reached an impressive level with the growing use of social media by the population. The extraction of data from social media, the various analysis and visualization methods are essential instruments in the study of dynamics of information flows and in the understanding of the mechanisms used for creating and spreading disinformation. GATE Institute is looking for experienced researchers who are interested in data extraction and analysis from social media for developing analyses tools for content in the Bulgarian language. The suitable applicants for this post should have a solid background, experience and publications in the domain of information and communication technologies, preferably with a specialization in one or more of the following domains: data retrieval, artificial intelligence, natural language processing. The practical experience in the development and testing of methods for social media analysis is an asset. Your responsibilities: - Designs, implements and supports complex data models data in the domain of social media analysis in Bulgarian and English; - Develops solutions for extracting data from different sources: text documents, relational databases, etc. - Develops models which help in the analysis of social media content; - Plans educational activities and teaches in programs in the domain of analysis of natural language and social media contents; - Participates in research on methods for analysis and content analysis in Bulgarian and other languages; - Evaluates new methods in comparison with the academic literature and the best international practice; - Develops ontologies to present the data semantics; - Contributes to the development of research projects in the domain of social media content analysis; - Keeps themselves up to date with the newest technological trends; - Contributes in publications with high research impact; - Participates in suitable activities for education and development. Requirements: - PhD Degree in computer science, computational linguistics, natural language processing, or another technological or philological area. Alternatively, a Master’s Degree in the same areas plus a minimum of 4 years of relevant experience in industry or academia; - Experience in the analysis of social media data; - Experience in the documentation and creation of datasets; - Experience in tools for collecting social media data, e.g. Crowdtangle, APIs for Twitter, YouTube; - Experience in developing models and machine learning, e.g. Python - Excellent written and oral communication and presentation skills; - Ability to prioritise tasks and work on multiple tasks and motivation for achieving high-quality results; - Understanding of the principles of Open Science; - Excellent knowledge of English language; - Preferably - knowledge of Bulgarian; - Experience in one or more of the following: - Experience and good knowledge of the concepts of natural language processing (text extraction, analysis, generation of texts in natural language) and processing of models for machine learning: Supervised and Unsupervised machine learning, Neural Networks, Support vector machines, Kernel methods; - experience in extracting data from social media. Our offer: - You will have the freedom to conduct research in any area within the scope and priorities of GATE, creating new visions for the future - You will be provided with numerous opportunities for learning, knowledge exchange and career development, locally and internationally - Your research will be supported by an advanced research infrastructure, comprising of the GATE platform and Open Innovation Labs - You will have a flexible work schedule and, a modern and appealing work environment, stirring up creativity and productivity - Competitive working conditions and a salary commensurate with your skills and experience How to apply Please send your contact data and files with personal documents on our Application Form – Gate (gate-ai.eu) <https://gate-ai.eu/en/application-form/> till 15 November 2022: - Cover/motivation letter that explains the motivating factors for considering the position (max. 1 pp), - CV with complete publication list, - Copies of diplomas for completed education and certificates of qualification, - Copies of documents certifying past work experience in the relevant field, - Brief description of important scientific achievements and scientific outlook (max. 2 pp), - Two references letters or personal recommendations, arranged by applicants and directly submitted by the letter or provided as contact data in the cover letter, - As an attachment to your application please sign and enclose the following declaration: *I agree to the processing of my personal data included in this application for the needs necessary to carry out the recruitment.* Additional information on the site www.gate-ai.eu or contact us on: milena.dobreva(a)gate-ai.eu By applying for these positions, you voluntarily provide your personal data and consent to be processed for the purpose of recruitment and selection of personnel. The processing of your personal data shall be carried out in accordance with the requirements of Regulation (EU) 2016/679 (General Data Protection Regulation), the Personal Data Protection Law and related legal acts in Bulgaria. -- *Irina Temnikova, Experienced scientific researcher* *Computational linguistics, Natural Language Processing* *Translation technologies* *Machine Translation, **Translation* *https://gate-ai.eu/en/staff/irina-temnikova-phd/ <https://gate-ai.eu/en/staff/irina-temnikova-phd/> * https://www.linkedin.com/in/irina-temnikova/ https://scholar.google.bg/citations?user=7BcpifAAAAAJ&hl=en ------------------------------- -------------------------------- -----

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CFP: Special Issue on The Role of Context in Neural Machine Translation Systems and its Evaluation
by Knowles, Rebecca 26 Oct '22

26 Oct '22

*Apologies for cross-posting* [image.png] CFP: Special issue on The Role of Context in Neural Machine Translation Systems and its Evaluation in Natural Language Engineering Guest editors: - Sheila Castilho (The ADAPT Centre, School of Applied Languages and Intercultural Studies, Dublin City University)) - Rebecca Knowles (National Research Council Canada) For this special issue, we invite the submission of papers focusing on the variety of novel implementations of context into neural machine translation systems as well as novel approaches to its evaluation. Recent claims that machine translation systems are reaching (near) human parity at the sentence level have been followed by subsequent analyses that indicate remaining gaps in translation quality at the document level. How best to evaluate machine translation at the document level (and what exactly constitutes document level evaluation) remains an open question. At the same time, there is work seeking to add discourse and context into neural machine translation systems. Papers that focus on topics of context in neural machine translation, machine translation evaluation, or both are welcome. For full details, see: https://sites.google.com/dcu.ie/nlecontextnmt/home Topics of interest include, but are not limited to: - Novel language processing techniques for implementing discourse in NMT systems - Document-level NMT and evaluation - Use of target and source context - Context-aware techniques for quality evaluation - Context-aware automatic and human evaluation metrics - The size and composition of the training data and its effect on context-aware systems - The effect of the quality of training data and test sets on context-aware systems - Translationese and its effect on document-level training - Lexical diversity and lexical density in discourse NMT - Discourse NMT for different domains Publication Timeline: - Articles deadline submission: 1 February 2023 - Return of reviews to contributors: 1 April 2023 - Revised articles deadline submission: 1 May 2023 - Return of second reviews to contributors (if applicable): 1 July 2023 - Final Submission: 15 September 2023 - Publication: November 2023 / January 2024 Format and Submission: Typical submissions will be 12-25 pages in length. Authors should follow the "Author Instructions" section on the journal website: https://www.cambridge.org/core/journals/natural-language-engineering/inform… We highly recommend using the LaTeX template found under "Preparing your materials" at the link above. All manuscripts must be submitted online via the NLE ScholarOne website: http://mc.manuscriptcentral.com/nle. Under "Special Issue Designation", choose "The Role of Context in Neural Machine Translation Systems and its Evaluation". Queries: Any queries related to this special issue should be addressed to sheila.castilho(a)dcu.ie<mailto:sheila.castilho@dcu.ie> with NLE-ContextNMT in the subject line.

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Internship: Training of an entity recognition model for nanobiology using distant supervision.
by Martin Lentschat 26 Oct '22

26 Oct '22

Context The NanoBubbles ERC Synergy project’s objective (https://nanobubbles.hypotheses.org<https://nanobubbles.hypotheses.%20org>) is to understand how, when and why science fails to correct itself. The project focuses on claims made within the field of nanobiology. Project members combine approaches from the natural sciences, computer science, and the social sciences and humanities (Science and Technology Studies) to understand how error correction in science works and what obstacles it faces. For this purpose, we aim to trace claims and corrections through various channels of scientific communication (journals, social media, advertisements, conference programs, etc.) via both qualitative and digital methods. Intership objectifs Entity recognition is an important step for downstream treatment in natural language processing. It consists in identifying the entities in a corpus belonging to a specific domain and in their labeling. Training methods relying on large annotated corpora are usually used for this purpose. However, such resource are not always available for specific domains, and alternative methods have to be employed (Hedderich 2020). Distant supervision (Mintz 2009) is a technique used to automatically label textual data using an external resource such as dictionaries (Shang 2018), gazetteers, ontologies (Wang 2021) and knowledge bases (Sun 2019). This enable the construction of a training corpus without the need of manual annotation. In specialized domains, this is especially useful in order to annotate complex and discontinuous entities with which human annotators may struggle (Khandelwal 2022). The objective of this internship is to implement a method to automatically annotate a corpus of scientific documents, using existing resources, in the nanobiology domain. After it, they will employ existing deep learning approaches (Liang 2020) to train an entity extraction model for entities in the nanobiology domain. Skills • Being enrolled in a Master in Natural Language Processing, computer science or data science. • Good programming skills in Python, including experiences with natural language processing tools and methods, knowledge of machine learning and deep learning frameworks and semantic web. • Ability to communicate and write in English is a plus. Scientific environment The work will be conducted within the Sigma team of the LIG laboratory (http://sigma.imag.fr). The recruited person will be welcomed within the team which offer a stimulating, multinational and pleasant working environment. Instructions for applying Applications must contain a CV + letter/message of motivation + master grades + letter(s) of recommendation (or names for potential letters), and be addressed to Cyril Labbé (cyril.labbe(a)imag.fr) and Amira Barhoumi (amira.barhoumi(a)univ-grenoble-alpes.fr). Applications will be considered on the fly. It is therefore advisable to apply as soon as possible. References • Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009, August). Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (pp. 1003-1011). • Shang, J., Liu, L., Ren, X., Gu, X., Ren, T., & Han, J. (2018). Learning named entity tagger using domain-specific dictionary. arXiv preprint arXiv:1809.03599. • Sun, Y., & Loparo, K. (2019, July). Information extraction from free text in clinical trials with knowledge-based distant supervision. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, pp. 954-955). IEEE. • Wang, X., Hu, V., Song, X., Garg, S., Xiao, J., & Han, J. (2021, November). CHEMNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 5227-5240). • Liang, C., Yu, Y., Jiang, H., Er, S., Wang, R., Zhao, T., & Zhang, C. (2020, August). Bond: Bert-assisted open-domain named entity recognition with distant supervision. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1054-1064). • Hedderich, M. A., Lange, L., Adel, H., Str ?otgen, J., & Klakow, D. (2020). A survey on recent approaches for natural language processing in low-resource scenarios. arXiv preprint arXiv:2010.12309. • Khandelwal, A., Kar, A., Chikka, V. R., & Karlapalem, K. (2022, May). Biomedical NER using Novel Schema and Distant Supervision. In Proceedings of the 21st Workshop on Biomedical Language Processing (pp. 155-160)

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Intership: Identification of scientific paper mentions on Twitter
by Martin Lentschat 26 Oct '22

26 Oct '22

Context The NanoBubbles ERC Synergy project’s objective (https://nanobubbles.hypotheses.org) is to understand how, when and why science fails to correct itself. The project focuses on claims made within the field of nanobiology. Project members combine approaches from the natural sciences, computer science, and the social sciences and humanities (Science and Technology Studies) to understand how error correction in science works and what obstacles it faces. For this purpose, we aim to trace claims and corrections through various channels of scientific communication (journals, social media, advertisements, conference programs, etc.) via both qualitative and digital methods. Intership objectifs Scientific articles are now discussed in a variety of mediums. The social network Twitter is particularly favored by several professionals, such as journalists and scientists, as a way of staying updated about recent development in their field, publicly discussed their work with distant colleagues and engage outside parties in their discoveries. Citing scientific articles on Twitter is easily done using publishers sharing links. Studies focusing on the use of social network by scientists (Costas 2015, 2017), the propagation of scientific information (Mohammadi 2018, W ?uhrl 2021, Hou 2022) and how the use of Twitter may influence back research (Ortega 2017). These studies rely heavily on the hyperlinks present in Twitter posts or on tools providing data on the use of research in social networks like PlumX (Champieux 2015). However, a scientific article citation can be present in a tweet as a ’fuzzy mention’ (e.g. I have read in a paper written by AUTHOR in 20XX that ...). These fuzzy mentions are hard to detect and need to be linked back to the article they refers to in order to be taken into considerations. The intern first task will consist in collecting a corpus of tweets containing such ’fuzzy mention’ of scientific articles. Afterwards he will apply existing extraction technics and models, mainly Named Entity Recognition, in order to extract the information enabling to (1) determine that a twitter post does mention an article and (2) link this article to a bibliographic database. Skills * Being enrolled in a Master in Natural Language Processing, computer science or data science. * Good programming skills in Python, including experiences with natural language processing tools and methods, knowledge of machine learning methods and deep learning models. * Curiosity for scientometrics. * Ability to communicate and write in English is a plus. Scientific environment The work will be conducted within the Sigma team of the LIG laboratory (http://sigma.imag.fr). The recruited person will be welcomed within the team which offer a stimulating, multinational and pleasant working environment. Instructions for applying Applications must contain a CV + letter/message of motivation + master grades + letter(s) of recommendation (or names for potential letters), and be addressed to Cyril Labbé (cyril.labbe(a)imag.fr) and Martin Lentschat (martin.lentschat(a)univ-grenoble-alpes.fr). Applications will be considered on the fly. It is therefore advisable to apply as soon as possible. References * Champieux, R. (2015). PlumX. Journal of the Medical Library Association: JMLA, 103(1), 63. * Costas, R., Mongeon, P., Ferreira, M. R., van Honk, J., & Franssen, T. (2020). Large-scale identification and characterization of scholars on Twitter. Quantitative Science Studies, 1(2), 771-791. * Costas, R., van Honk, J., & Franssen, T. (2017). Scholars on Twitter: who and how many are they?. arXiv preprint arXiv:1712.05667. * Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K. L. (2018). Academic information on Twitter: A user survey. PloS one, 13(5), e0197265. * Hou, J., Wang, Y., Zhang, Y., & Wang, D. (2022). How do scholars and non-scholars participate in dataset dissemination on Twitter. Journal of Informetrics, 16(1), 101223. * Wührl, A., & Klinger, R. (2021). Claim detection in biomedical Twitter posts. arXiv preprint arXiv:2104.11639. * Ortega, J. L. (2017). The presence of academic journals on Twitter and its relationship with dissemination (tweets) and research impact (citations). Aslib journal of information management, 69(6), 674-687.

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PhD studentships: UKRI CDT in NLP, University of Edinburgh
by Alex Lascarides 26 Oct '22

26 Oct '22

FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS - UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING Based at the University of Edinburgh: in conjunction with the School of Informatics and School of Philosophy, Psychology and Language Sciences. Deadlines: * Non UK : 25th November 2022 * UK : 27th January 2023 Applications are now sought for the UKRI CDT in NLP’s fifth and final cohort of students, which will start in September 2023. * * * The CDT in NLP offers unique, tailored doctoral training comprising both taught courses and a doctoral dissertation over four years. Each student will take a set of courses designed to complement their existing expertise and give them an interdisciplinary perspective on NLP. The studentships are fully funded for the four years and come with a generous allowance for travel, equipment and research costs. The CDT brings together researchers in NLP, speech, linguistics, cognitive science and design informatics from across the University of Edinburgh. Students will be supervised by a world-class faculty comprising almost 60 supervisors and will benefit from cutting edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality and visualisation labs. The CDT involves a number of industrial partners, including Amazon, Facebook, Huawei, Microsoft, Naver, Toshiba, and the BBC. Links also exist with the Alan Turing Institute and the Bayes Centre. A wide range of research topics fall within the remit of the CDT: * Natural language processing and computational linguistics * Speech technology * Dialogue, multimodal interaction, language and vision * Information retrieval and visualization, computational social science * Computational models of human cognition and behaviour, including language and speech processing * Human-Computer interaction, design informatics, assistive and educational technology * Psycholinguistics, language acquisition, language evolution, language variation and change * Linguistic foundations of language and speech processing. The next cohort of CDT students will start in September 2023. Around 12 studentships are available, covering maintenance at the UKRI rate (currently £17,668 per year) plus tuition fees. Studentships are open to all nationalities and we are particularly keen to receive applications from women, minority groups and members of other groups that are underrepresented in technology. Applicants in possession of other funding scholarships or industry funding are also welcome to apply – please provide details of your funding source on your application. Applicants should have an undergraduate or master’s degree in computer science, linguistics, cognitive science, AI, or a related discipline; or have a breadth of relevant experience in industry/academia/public sector, etc. Further details, including the application procedure, can be found at: https://edin.ac/cdt-in-nlp Application Deadlines: Early application is encouraged but completed applications must be received at the latest by: * 25th November 2022 (non UK applicants) or 27th January 2023 (UK applicants). Enquiries: Please direct any enquiries to the CDT admissions team at: cdt-nlp-info(a)inf.ed.ac.uk. CDT in NLP Virtual Open Day: Find out more about the programme by attending the PG Virtual Open Week in November. Click here to register: https://www.ed.ac.uk/studying/postgraduate/open-days-events-visits/open-day… -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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DEADLINE EXTENSION: TLT 2023, 3rd call for papers
by Kilian Evang 26 Oct '22

26 Oct '22

The 21st International Workshop on Treebanks and Linguistic Theories – THIRD CALL FOR PAPERS Submission link: https://openreview.net/group?id=georgetown.edu/GURT/2023/Conference Submission deadline (extended): Nov 15th, 2022 Invited speakers announced (scroll down) The 21st International Workshop on Treebanks and Linguistic Theories (TLT) will bring together developers and users of linguistically annotated natural language corpora and take place during the week of March 9th–12th, 2023 in Washington D.C. on the campus of Georgetown University as part of GURT 2023. VENUE The Georgetown University Round Table on Linguistics (GURT) is a peer-reviewed annual linguistics conference held continuously since 1949 at Georgetown University in Washington DC, with topics and co-located events varying from year to year. Under an overarching theme of ‘Computational and Corpus Linguistics’, GURT 2023 will feature four events, which are workshops or conferences focused on computational and corpus approaches to syntax but also covering theoretical issues: Universal Dependency Workshop (UDW), Depling, Treebanks and Linguistic Theory (TLT), and CxGs+NLP. All talks from all events will take place in a single (non-parallel) plenary session, with the papers from one event being presented contiguously. The goal of co-locating these events is to promote cross-fertilization of ideas across subcommunities. Proceedings will be published separately for each event, and will be available in the ACL Anthology. In order to support rich discussions and networking with minimal overhead and cost, GURT will be primarily an in-person event; we will, however, accommodate a limited number of live/synchronous remote presentations, prioritizing those with circumstances that prevent travel. University policies regarding COVID safety will be in force during the event. Georgetown University is located in a historic neighborhood in the heart of the nation’s capital. The city is a premier tourist destination, and the region is served by Reagan National (DCA), Dulles (IAD), and Baltimore-Washington (BWI) airports. GURT INVITED SPEAKERS - Jonathan Dunn, University of Canterbury, New Zealand (CxGs+NLP) - Guy Perrier, Loria, France (Depling) - Joan Bresnan, Stanford University, USA (TLT) - Joakim Nivre, Uppsala University, Sweden (UDW) 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 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: - long papers on substantial, original, and unpublished research, including empirical evaluation results, where appropriate; - short papers on smaller, focused contributions, work in progress, negative results, surveys, or opinion pieces. All papers accepted for presentation at the workshop will be included in the TLT 2023 proceedings volume, which will be part of the ACL Anthology. 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 All 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: https://openreview.net/group?id=georgetown.edu/GURT/2023/Conference Double submission policy: We will accept submissions that have been or will be submitted elsewhere, but require that the authors notify us, including information on where else they are submitting. We also require that authors withdraw work that will be published elsewhere (no double publication). Submissions that violate these requirements will be rejected without review. All papers will be refereed through a double-blind peer review process with final acceptance decisions made by the workshop organizers. Submissions may be selected for publication in a GURT venue other than TLT at the discretion of the organizers. IMPORTANT DATES Long and short paper submission deadlines: November 15th, 2022 Reviews Due: December 17th, 2022 Notification of acceptance: January 11th, 2023 Final version of papers due: February 1st, 2023 GURT2023: March 9th-12th, 2023 TLT WORKSHOP CHAIRS Daniel Dakota, Indiana University Kilian Evang, Heinrich Heine University Düsseldorf Sandra Kübler, Indiana University Lori Levin, Carnegie Mellon University Contact: ddakota(a)iu.edu Website: https://cl.indiana.edu/tlt2023 GURT Website: https://gurt.georgetown.edu/

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Call for Applications: Study NLP at UC Santa Cruz
by Emelye Neff 25 Oct '22

25 Oct '22

The UCSC Natural Language Processing (NLP) master’s degree program <http://nlp.ucsc.edu> provides both depth and breadth in core algorithms and methods for NLP. Taught intensively over 15-18 months, our program design combines theoretical learning with hands-on practice to ensure our students have the right skill set to prepare for a professional career in this fast-growing field. We are currently accepting applications for Fall 2023 admission consideration. Program Highlights: - A 15-18 month program with a Capstone project mentored by NLP faculty and industry experts. - NLP students get an in-depth, systematic education in NLP, machine learning, and data science and analytics with faculty who have both academic and industry experience. - Our Industry Advisory Board provides insight and career advice through mentoring, guest lectures, and attendance at networking and professional development events. - All our courses are exclusive to NLP students and are designed with input from our Industry Advisory Board ensuring the content is current, relevant, and grounded in real-world context. - Our program is based at state-of-the-art facilities at the UCSC Silicon Valley Campus, located in Santa Clara, California. Applying to the NLP MS Program This program is intended for students with a strong background in computer science. We are looking for multifaceted individuals with solid skills in programming, algorithms, machine learning, probability, statistics, and linguistics. Visit our Admissions page <http://nlp.ucsc.edu/admissions> to review admission requirements and tips for applying, and to connect with our support team. Applications for Fall 2023 admission consideration are now open. Apply by March 1, 2023 <https://applygrad.ucsc.edu/apply/>. If you have questions about the program or the application process, please contact the NLP Support Team <nlp(a)ucsc.edu>. All the best, The UCSC NLP Program Team Baskin Engineering University of California, Santa Cruz

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Adjunct Faculty positions at Open University of Cyprus
by Loizos Michael 25 Oct '22

25 Oct '22

Dear colleagues, The Open University of Cyprus is looking for Adjunct Faculty members for the following two courses offered as part of the M.Sc. in Cognitive Systems program of studies (https://www.ouc.ac.cy/index.php/en/studies/master/cos): COS524: "Natural Language Processing" (spring semester) COS613: "Cognitive Agents and Reasoning" (fall semester) Courses are offered in English, through a distance-learning methodology. Details about the application process are here: https://www.ouc.ac.cy/index.php/en/news-events/news/2595-sepx2022 Regards, Loizos

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