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First Call for Papers: Fourth Biennial Conference on Language, Data and Knowledge (LDK 2023)
by Fahad Khan 27 Oct '22

27 Oct '22

============== Call for Papers @ Fourth Biennial Conference on Language, Data and Knowledge (LDK 2023) Dates: 12–13 September 2023 (Workshops/Tutorials day), 14–15 September 2023 (Main Conference) Location: Vienna, Austria Website: http://2023.ldk-conf.org Submission Deadline: 10 March 2023 ============== We invite submissions for the fourth biennial conference on Language, Data and Knowledge (LDK 2023) to be held in Vienna, Austria in September 2023. This conference aims to bring together researchers from across different disciplines concerned with the acquisition, treatment, curation and use of language data in the context of data science and knowledge-based applications. This edition builds upon the success of the inaugural event held in Galway, Ireland in 2017, the second LDK in Leipzig, Germany in 2019, and the third LDK in Zaragoza, Spain in 2021. Paper submission We welcome submission of relevance to the topics listed below. Submissions can be in the form of: - Long papers: 9–12 pages; - Short papers: 4–6 pages. All submission lengths are given including references. Accepted submissions will be published by ACL in an open-access conference proceedings volume, free of charge for authors. The ACL templates <https://github.com/acl-org/acl-style-files> should therefore be used for all conference submissions. As the reviewing process is single-blind, submissions should not be anonymized. More information will be provided soon regarding the submission platform and respective link. Presentation format Accepted submissions will be selected for oral or poster presentation based on recommendations from the reviewers. This decision will not reflect any difference in the quality of the papers, and there will be no distinction between oral and poster presentations in the published proceedings. Authors of accepted short papers or posters are welcome to present their work as a demo in addition to the regular presentation. The conference will be hybrid (face-to-face and remote). Note that at least one author of each accepted paper must register to present the paper at the conference (either remotely or on-site). There will be no registration fee administered for participating in LDK 2023. Topics Relevant topics for the conference include, but are not limited to, the following fields: Language Data - Language data construction and acquisition - Language data annotation - FAIR data practices for language data - Language data portals and metadata about language data - Organisational and infrastructural management of language data - Multilingual, multimedia and multimodal language data - Evaluation, provenance and quality of language data - Visualisation of language data - Standards and interoperability of language data - Legal aspects of publishing language data - Under-resourced languages - e-Lexicography - Semantic processing Knowledge Graphs - Linguistic linked data and the multilingual semantic web - Ontologies, terminologies, wordnets, framenets and related resources - Information and knowledge extraction (taxonomy extraction, ontology learning) - Data, information and knowledge integration across languages - (cross-lingual) ontology alignment - Entity linking and relatedness - Linked data profiling - Knowledge representation and reasoning - Knowledge graphs for corpora processing and analysis Applications for Language, Data and Knowledge - Question answering and semantic search - Text analytics on big data - NLP for language documentation and preservation - Speech recognition and synthesis - Spoken language processing - Semantic content management - Computer-aided language learning - Natural language interfaces to big data - Knowledge-based NLP - Deep learning and machine learning for and on LLOD - Other applications Use Cases in Language, Data and Knowledge Contributions are welcome where the topics above - and others within the scope of Language, Data and Knowledge - are applied to domain-specific use cases, including but not limited to social sciences and humanities, legal, life sciences, FinTech, cybersecurity. Organising committee Conference Chairs - Jorge Gracia – University of Zaragoza - John P. McCrae – University of Galway Program Chairs: - Sara Carvalho – University of Aveiro - Anas Fahad Khan – Institute for Computational Linguistics “A. Zampolli” Workshop and tutorial chairs: - Ana Ostroški Anić – Institute of Croatian Language and Linguistics - Blerina Spahiu – University of Milano-Bicocca Local Chair: - Dagmar Gromann – University of Vienna Proceedings Chair: - Ana Salgado – Lisbon Academy of Sciences Important Dates 10 March 2023 Paper submission deadline 28 April 2023 Notification 26 May 2023 Camera-ready submission deadline 12–13 September 2023 Pre-conference events 14–15 September 2023 Main conference Note that a call for workshop proposals is to follow with a submission deadline for 19th of December. All deadlines refer to anywhere-on-earth time. Program Committee (to be announced)

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Internship : Study on the accuracy of citations in scientific papers
by AMIRA BARHOUMI 27 Oct '22

27 Oct '22

Call for Internship applications in Natural Language Processing Title : Study on the accuracy of citations in scientific papers Starting date : February 2023 Application deadline : December 5th, 2022 Location: LIG laboratory, Grenoble Alps University, France Keywords: Natural language processing, Scientific literature, citation accuracy 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. Internship objectives : In scientific papers, citations acknowledge the sources and help the reader to find more information about the citation context. Citations are also an important indicator ex ploited to identify significant publications in a specific scientific field (Arag on 2013). They are used for different purposes, e.g. referring to state of the art, to a specific method or result, and they reflect how authors frame their work and this diversity impacts future academics adoption (Jurgens 2018). Recently, there have been numerous research in Natural Language Processing on citation analysis in scientific literature. Studies of citation behavior aim at understanding how researchers cited a paper in their work. Existing works on citation analysis deal with determining citation sentiment (Liu 2017, Athar 2011), finding out citation function (Yu 2020, Pride 2019, Bakhti 2018) and identifying criti cal citation contexts (Te 2022). Nevertheless, studies that evaluate the accuracy of citations are scarce. Studies on the accuracy of citations in various scientific disciplines demonstrate an error rate of 25%-54% (Jergas 2015, Siebers 2000, Kristof 1997, Key 1977). These errors alter the original content and meaning of the cited paper. They can range from minor to major errors in citation accuracy. Several studies describe various issues that may arise when citing original research done by others. For example, in the following sentence “ it has been shown that bubblegum is much more pink than flamingo while running very fast [Einstein A., 1916] ”: * “ [Einstein A., 1916] ” represents the "citation" * The citation refers to the following scientific paper “ Einstein, A. (1916 (translation 1920)), Relativity: The Special and General Theory ” * The Einstein’s paper represents the cited paper * The cited paper is not correlated with the meaning of the sentence, i.e. there is no relation between the colors and the relativity notion. The aim of this internship is to assess the content of both cited and citing papers in scientific literature, i.e. study the correlation between the citation and its context in the citing paper in order to identify miss-citations. The intern tasks would then be to (1) test and compare unsupervised NLP methods and pre-trained embedding models (SciBert, BioBert, etc. ) in order to measure the accuracy of citations using available datasets, and to (2) provide project members with a set of reliable tools. Skills : * Being enrolled in a Master in Natural Language Processing, computer science or data science. * Good programming skills in Python, experience with natural language processing tools and frameworks, knowledge of machine learning methods and deep learning technics. * Ability to communicate and write in English is a plus Scientific environment : The work will be conducted within the Sigma team of the LIG labora tory (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 + mas ter 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). Applica tions will be considered on the fly. It is therefore advisable to apply as soon as possible. References : * (Arag on 2013) Arag on M. A measure for the impact of research. Scientific reports. 2013;3(1):1–5. * (Jurgens 2018) Jurgens D, Kumar S, Hoover R, Mc-Farland D, Jurafsky D. Measuring the Evo lution of a Scientific Field through Citation Frames. Transactions of the Association for Com putational Linguistics. 2018;6:391–406. * (Jergas 2015) Jergas H, Baethge C. Quotation accuracy in medical journal articles-a systematic review and meta-analysis. PeerJ. 2015;3:e1364. * (Kristof 1997) Kristof C. Accuracy of reference citations in five entomology journals. Am Ento mol. 1997;43(4):246-251. * (Key 1977) Key JD, Roland CG. Reference accuracy in articles accepted for publication in the Archives of Physical Medicine and Rehabilitation. Arch Phys Med Rehabil. 1977;58(3):136-137. * (Siebers 2000) Siebers R, Holt S. Accuracy of references in five leading medical journals. Lancet. 2000;356(9239):1445. * (Te 2022) Te S, Barhoumi A, Lentschat M, Bordignon F, Labb ? e C, Portet F. Citation Context Classification: Critical vs Non-critical. In proceedings of the Third Workshop on Scholarly Document Processing. 2022:49-53. * (Liu 2017) Liu H. Sentiment analysis of citations using word2vec. 2017;CoRR, abs/1704.00177. * (Athar 2011) Athar A. Sentiment analysis of citations using sentence structure-based features. In Proceedings of the ACL 2011 Student Session. 2011:81–87. * (Bakhti 2018) Bakhti K, Niu Z, Yousif A, Nyamawe A. Citation Function Classification Based on Ontologies and Convolutional Neural Networks. 2018:105–115. * (Pride 2019) Pride D, Knoth P, Jozef Harag J. Act: An annotation platform for citation typing at scale. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL). 2019:329–330. * (Yu 2020) Yu W, Yu M, Zhao T, Jiang M. Identifying referential intention with heterogeneous contexts. 2020:962–972.

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ESSLLI 2023: Call for Course and Workshop Proposals
by Kilian Evang 27 Oct '22

27 Oct '22

CALL FOR COURSE AND WORKSHOP PROPOSALS 34th European Summer School in Logic, Language and Information – ESSLLI 2023 31 July–11 August, 2023, Ljubljana, Slovenia https://2023.esslli.eu/ Important Dates =============== 13 January 2023: Course Title submission deadline (mandatory) 20 January 2023: Final submission 3 March 2023: Notification Introduction ============ Under the auspices of the Association for Logic, Language, and Information (FoLLI), the European Summer School in Logic, Language, and Information (ESSLLI) runs every year. Except for 2021, where the school was virtual, it runs in a different European country each year. It takes place over two weeks in the summer, hosts approximately 50 different courses at both introductory and advanced levels, and attracts around 400 participants from all over the world. Since 1989, ESSLLI has been providing outstanding interdisciplinary educational opportunities in the fields of Computer Science, Cognitive Science, Linguistics, Logic, Philosophy, and beyond. It comes from a community which recognizes that advances in our common areas require the contributions of multiple interrelated disciplines. The main focus of ESSLLI is the interface between linguistics, logic and computation, with special emphasis in human linguistic and cognitive ability. Courses, both introductory and advanced, cover a wide variety of topics within the combined areas of interest: Logic and Computation, Computation and Language, and Language and Logic. Workshops are also organized, providing opportunities for in-depth discussion of issues at the forefront of research, as well as a series of invited evening lectures. Topics and Format ================= Proposals for courses and workshops at ESSLLI 2023 are invited in all areas of Logic, Linguistics and Computer Sciences. Cross-disciplinary and innovative topics are particularly encouraged. Each course and workshop will consist of five 90 minute sessions, offered daily (Monday-Friday) in a single week. Proposals for two-week courses should be structured and submitted as two independent one-week courses, e.g. as an introductory course followed by an advanced one. In such cases, the ESSLLI program committee reserves the right to accept just one of the two proposals. All instructional and organizational work at ESSLLI is performed completely on a voluntary basis, so as to keep participation fees to a minimum. However, organizers and instructors have their registration fees waived, and are reimbursed for travel and accommodation expenses up to a level to be determined and communicated with the proposal notification. ESSLLI can only guarantee reimbursement for at most one course/workshop organizer, and cannot guarantee full reimbursement of travel costs for lecturers or organizers from outside of Europe. The ESSLLI organizers would appreciate any help in controlling the School's expenses by seeking partial or complete coverage of travel and accommodation expenses from other sources. Categories ========== Each proposal should fall under one of the following categories. Foundational Courses -------------------- Such courses are designed to present the basics of a research area, to people with no prior knowledge in that area. They should be of elementary level, without prerequisites in the course's topic, though possibly assuming a level of general scientific maturity in the relevant discipline. They should enable researchers from related disciplines to develop a level of comfort with the fundamental concepts and techniques of the course's topic, thereby contributing to the interdisciplinary nature of our research community. Introductory Courses -------------------- Introductory courses are central to ESSLLI's mission. They are intended to introduce a research field to students, young researchers, and other non-specialists, and to foster a sound understanding of its basic methods and techniques. Such courses should enable researchers from related disciplines to develop some comfort and competence in the topic considered. Introductory courses in a cross-disciplinary area may presuppose general knowledge of the related disciplines. Advanced Courses ---------------- Advanced courses are targeted primarily to graduate students who wish to acquire a level of comfort and understanding in the current research of a field. Workshops --------- Workshops focus on specialized topics, usually of current interest. Workshop organizers are responsible for soliciting papers and selecting the workshop program. They are also responsible for publishing proceedings if they decide to have proceedings. Proposal Guidelines =================== Course and workshop proposals should closely follow these guidelines to ensure full consideration. Course and Workshop proposals can be submitted by no more than two lecturers/organizers and they are presented by no more than these two lecturers/organizers. All instructors and organizers must possess a PhD or equivalent degree by the submission deadline. Course proposals should mention explicitly the intended course category. Proposals for introductory courses should indicate the intended level, for example as it relates to standard textbooks and monographs in the area. Proposals for advanced courses should specify the prerequisites in detail. Proposals of Courses given at ESSLLI the previous year will have a lower priority of being accepted in the current year. Proposals must be in PDF format include all of the following: 1. Personal information for each proposer: Name, affiliation, contact address, email, homepage (optional) 2. General proposal information: Title, category 3. Contents information: a. Abstract of up to 150 words b. Motivation and description (up to two pages) c. Tentative outline d. Expected level and prerequisites e. Appropriate references (e.g. textbooks, monographs, proceedings, surveys) 4. Information on the proposer and course: a. Will your course appeal to students outside of the main discipline of the course? b. Include information on your experience in the intensive one-week interdisciplinary setting c. Include evidence that the course proposer is an excellent lecturer 5. Information from workshop organizers: a. Include information on relevant preceding meetings and events, if applicable b. Include information about potential external funding for participants Submission Information ====================== **By January 13, 2023:** You are asked to submit in EasyChair at least the name(s) of the instructor(s), the ESSLLI area+course level and a short abstract. **By January 20:** Your submission must be completed by uploading a PDF with the actual proposal as detailed above. Submission Portal ================= Please submit your proposals to https://easychair.org/conferences/?conf=esslli2023 Childcare ========= If there is enough interest, ESSLLI will provide information on private child care services available during the summer school. EACSL Sponsorship ================= The EACSL will support one Logic and Computation course or workshop addressing topics of interest to Computer Science Logic (CSL) conferences. The selected course or workshop will be designated an EACSL course/workshop in the programme. If you wish to be considered for this, please indicate so in your proposal. Organizing Committee ==================== Slavko Žitnik (University of Ljubljana) (chair) Špela Vintar (University of Ljubljana) Timotej Knez (University of Ljubljana) Mojca Brglez (University of Ljubljana) Matej Klemen (University of Ljubljana) Aleš Žagar (University of Ljubljana) Program Committee ================= Juha Kontinen (University of Helsinki) (chair) Kaja Dobrovoljc (University of Ljubljana) (local co-chair) Area Chairs Language and Computation (LaCo) ------------------------------------------- Kilian Evang (Heinrich Heine University Düsseldorf) Miryam de Lhoneux (KU Leuven) Shane Steinert-Threlkeld (University of Washington) Area Chairs Language and Logic (LaLo) ------------------------------------- Ivano Ciardelli (University of Padua) Agata Renans (Ruhr-Universität Bochum) Jacopo Romoli (Heinrich Heine University Düsseldorf) Area Chairs Logic and Computation (LoCo) ---------------------------------------- Natasha Alechina (Utrecht University) Alessandra Palmigiano (Vrije Universiteit Amsterdam) Lutz Straßburger (Inria Saclay & École Polytechnique) ESSLLI Steering Committee ========================= Darja Fiser (University of Ljubljana) (chair) Phokion Kolaitis (University of California, Santa Cruz) (vice-chair) Roman Kuznets (TU Wien) Petya Osenova (Bulgarian Academy of Sciences and Sofia University) (secretary) Jakub Szymanik (University of Amsterdam)

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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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