January 2023 - Corpora

3-year PhD position in Automatic Argumentation Mining in French Legal Decisions (Inria Lille, University of Lille, and LexisNexis France)
by Pascal Denis 10 Nov '23

10 Nov '23

Hi there, Could you please distribute the following job offer? Thanks. Best, Pascal ------------------------------------------------------------------------------------- We invite applications for a 3-year PhD position co-funded by Inria, the French national research institute in Computer Science and Applied Mathematics, and LexisNexis France, leader of legal information in France and subsidiary of the RELX Group. The overall objective of this project is to develop an automated system for detecting argumentation structures in French legal decisions, using recent machine learning-based approaches (i.e. deep learning approaches). In the general case, these structures take the form of a directed labeled graph, whose nodes are the elements of the text (propositions or groups of propositions, not necessarily contiguous) which serve as components of the argument, and edges are relations that signal the argumentative connection between them (e.g., support, offensive). By revealing the argumentation structure behind legal decisions, such a system will provide a crucial milestone towards their detailed understanding, their use by legal professionals, and above all contributes to greater transparency of justice. The main challenges and milestones of this project start with the creation and release of a large-scale dataset of French legal decisions annotated with argumentation structures. To minimize the manual annotation effort, we will resort to semi-supervised and transfer learning techniques to leverage existing argument mining corpora, such as the European Court of Human Rights (ECHR) corpus, as well as annotations already started by LexisNexis. Another promising research direction, which is likely to improve over state-of-the-art approaches, is to better model the dependencies between the different sub-tasks (argument span detection, argument typing, etc.) instead of learning these tasks independently. A third research avenue is to find innovative ways to inject the domain knowledge (in particular the rich legal ontology developed by LexisNexis) to enrich enrich the representations used in these models. Finally, we would like to take advantage of other discourse structures, such as coreference and rhetorical relations, conceived as auxiliary tasks in a multi-tasking architecture. The successful candidate holds a Master's degree in computational linguistics, natural language processing, machine learning, ideally with prior experience in legal document processing and discourse processing. Furthermore, the candidate will provide strong programming skills, expertise in machine learning approaches and is eager to work at the interplay between academia and industry. The position is affiliated with the MAGNET [1], a research group at Inria, Lille, which has expertise in Machine Learning and Natural Language Processing, in particular Discourse Processing. The PhD student will also work in close collaboration with the R&D team at LexisNexis France, who will provide their expertise in the legal domain and the data they have collected. Applications will be considered until the position is filled. However, you are encouraged to apply early as we shall start processing the applications as and when they are received. Applications, written in English or French, should include a brief cover letter with research interests and vision, a CV (including your contact address, work experience, publications), and contact information for at least 2 referees. Applications (and questions) should be sent to Pascal Denis (pascal.denis(a)inria.fr). The starting date of the position is 1 November 2022 or soon thereafter, for a total of 3 full years. Best regards, Pascal Denis [1] https://team.inria.fr/magnet/ [2] https://www.lexisnexis.fr/ -- Pascal ---- Pour une évaluation indépendante, transparente et rigoureuse ! Je soutiens la Commission d'Évaluation de l'Inria. ---- +++++++++++++++++++++++++++++++++++++++++++++++ Pascal Denis Equipe MAGNET, INRIA Lille Nord Europe Bâtiment B, Avenue Heloïse Parc scientifique de la Haute Borne 59650 Villeneuve d'Ascq Tel: ++33 3 59 35 87 24 Url: http://researchers.lille.inria.fr/~pdenis/ +++++++++++++++++++++++++++++++++++++++++++++++

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Core metadata scheme for learner corpora - feedback needed!
by Magali Paquot 30 Oct '23

30 Oct '23

Dear colleagues, Last month, we shared the result of our collaborative work on a core metadata scheme for learner corpora with LCR2022 participants. Our proposal builds on Granger and Paquot (2017)'s first attempt to design such a scheme and during our presentation, we explained the rationale for expanding on the initial proposal and discussed selected aspects of the revised scheme. Our proposal is available at https://docs.google.com/spreadsheets/d/1-RbX5iUCUtCBkZU9Rfk-kv-Vzc--F-eUW2O…<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…> We firmly believe that our efforts to develop a core metadata scheme for learner corpora will only be successful to the extent that (1) the LCR community is given the opportunity to engage with our work in various ways (provide feedback on the general structure of the scheme, the list of variables that we identified as core and their operationalization; test the metadata on other learner corpora; use the scheme to start a new corpus compilation, etc.) and (2) the core metadata scheme is the result of truly collaborative work. As mentioned at LCR2022, we will be collecting feedback on the metadata scheme until the end of October. The online feedback form is available at: https://docs.google.com/document/d/1NeDUuxGJlPSJI9wHVA1xgGM-aV8jXTa8Qlb45K-…<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…> We'd like to thank all the colleagues who already got back to us (at LCR2022, by email or via the online form). We also thank them for their appreciation and enthusiasm for our work! We'd also like to encourage more colleagues (and particularly those of you who have experience in learner corpus compilation) to provide feedback! We need help in finalizing the core metadata scheme to make sure that it can be applied in all learner compilation contexts. In short, we need you to make sure the scheme meets the needs of the LCR community at large. With very best wishes, Magali Paquot (also on behalf of Alexander König, Jennifer-Carmen Frey, and Egon W. Stemle) Reference Granger, S. & M. Paquot (2017). Towards standardization of metadata for L2 corpora. Invited talk at the CLARIN workshop on Interoperability of Second Language Resources and Tools, 6-8 December 2017, University of Gothenburg, Sweden. Dr. Magali Paquot Centre for English Corpus Linguistics Institut Langage et Communication UCLouvain https://perso.uclouvain.be/magali.paquot/

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New articles for Asia Pacific Journal of Corpus Research (APJCR) Vol. 3, No. 1 are available online (Open Access)
by Prof CK Jung 08 Jul '23

08 Jul '23

Dear all Just wanted to let you know that APJCR Vol. 3, No. 1 is now available to view online. http://icr.or.kr/ejournals-apjcr CK --- *CK Jung BEng(Hons) Birmingham MSc Warwick EdD Warwick Cert Oxford* Department of English Language and Literature, Incheon National University, *South Korea* Vice President | The Korea Association of Primary English Education (KAPEE), *South Korea* Vice President | The Korea Association of Secondary English Education (KASEE), *South Korea* Director | Institute for Corpus Research, Incheon National University, *South Korea* (http://icr.or.kr) Editor | Asia Pacific Journal of Corpus Research, ICR, *International* ( http://icr.or.kr/apjcr) Deputy Editor | Korean Journal of English Language and Linguistics, KASELL, *South Korea* Editorial Board | Corpora, Edinburgh University Press, *UK* Editorial Board | English Today, Cambridge University Press, *UK* E: ckjung(a)inu.ac.kr / T: +82 (0)32 835 8129 H(EN): http://ckjung.org H(KR): http://prof1.inu.ac.kr/user/ckjung

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PhD in ML/NLP – Efficient, Fair, robust and knowledge informed self-supervised learning for speech processing
by François Portet 01 Jun '23

01 Jun '23

PhD in ML/NLP – Efficient, Fair, robust and knowledge informed self-supervised learning for speech processing Starting date: November 1st, 2022 (flexible) Application deadline: September 5th, 2022 Interviews (tentative): September 19th, 2022 Salary: ~2000€ gross/month (social security included) Mission: research oriented (teaching possible but not mandatory) *Keywords:*speech processing, natural language processing, self-supervised learning, knowledge informed learning, Robustness, fairness *CONTEXT* The ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies) will start on November 1st 2022. Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks. *PROJECT OBJECTIVES* Recent SSL models for speech such as HuBERT or wav2vec 2.0 have shown an impressive impact on downstream tasks performance. This is mainly due to their ability to benefit from a large amount of data at the cost of a tremendous carbon footprint rather than improving the efficiency of the learning. Another question related to SSL models is their unpredictable results once applied to realistic scenarios which exhibit their lack of robustness. Furthermore, as for any pre-trained models applied in society, it isimportant to be able to measure the bias of such models since they can augment social unfairness. The goals of this PhD position are threefold: - to design new evaluation metrics for SSL of speech models ; - to develop knowledge-driven SSL algorithms ; - to propose methods for learning robust and unbiased representations. SSL models are evaluated with downstream task-dependent metrics e.g., word error rate for speech recognition. This couple the evaluation of the universality of SSL representations to a potentially biased and costly fine-tuning that also hides the efficiencyinformation related to the pre-training cost. In practice, we will seek to measure the training efficiency as the ratio between the amount of data, computation and memory needed to observe a certain gain in terms of performance on a metric of interest i.e.,downstream dependent or not. The first step will be to document standard markers that can be used as robust measurements to assess these values robustly at training time. Potential candidates are, for instance, floating point operations for computational intensity, number of neural parameters coupled with precision for storage, online measurement of memory consumption for training and cumulative input sequence length for data. Most state-of-the-art SSL models for speech rely onmasked prediction e.g. HuBERT and WavLM, or contrastive losses e.g. wav2vec 2.0. Such prevalence in the literature is mostly linked to the size, amount of data and computational resources injected by thecompany producing these models. In fact, vanilla masking approaches and contrastive losses may be identified as uninformed solutions as they do not benefit from in-domain expertise. For instance, it has been demonstrated that blindly masking frames in theinput signal i.e. HuBERT and WavLM results in much worse downstream performance than applying unsupervised phonetic boundaries [Yue2021] to generate informed masks. Recently some studies have demonstrated the superiority of an informed multitask learning strategy carefully selecting self-supervised pretext-tasks with respect to a set of downstream tasks, over the vanilla wav2vec 2.0 contrastive learning loss [Zaiem2022]. In this PhD project, our objective is: 1. continue to develop knowledge-driven SSL algorithms reaching higher efficiency ratios and results at the convergence, data consumption and downstream performance levels; and 2. scale these novel approaches to a point enabling the comparison with current state-of-the-art systems and therefore motivating a paradigm change in SSL for the wider speech community. Despite remarkable performance on academic benchmarks, SSL powered technologies e.g. speech and speaker recognition, speech synthesis and many others may exhibit highly unpredictable results once applied to realistic scenarios. This can translate into a global accuracy drop due to a lack of robustness to adversarial acoustic conditions, or biased and discriminatory behaviors with respect to different pools of end users. Documenting and facilitating the control of such aspects prior to the deployment of SSL models into the real-life is necessary for the industrial market. To evaluate such aspects, within the project, we will create novel robustness regularization and debasing techniques along two axes: 1. debasing and regularizing speech representations at the SSL level; 2. debasing and regularizing downstream-adapted models (e.g. using a pre-trained model). To ensure the creation of fair and robust SSL pre-trained models, we propose to act both at the optimization and data levels following some of our previous work on adversarial protected attribute disentanglement and the NLP literature on data sampling and augmentation [Noé2021]. Here, we wish to extend this technique to more complex SSL architectures and more realistic conditions by increasing the disentanglement complexity i.e. the sex attribute studied in [Noé2021] is particularly discriminatory. Then, and to benefit from the expert knowledge induced by the scope of the task of interest, we will build on a recent introduction of task-dependent counterfactual equal odds criteria [Sari2021] to minimize the downstream performance gap observed in between different individuals of certain protected attributes and to maximize the overall accuracy. Following this multi-objective optimization scheme, we will then inject further identified constraints as inspired by previous NLP work [Zhao2017]. Intuitively, constraints are injected so the predictions are calibrated towards a desired distribution i.e. unbiased. *SKILLS* * Master 2 in Natural Language Processing, Speech Processing, computer science or data science. * Good mastering of Python programming and deep learning framework. * Previous in Self-Supervised Learning, acoustic modeling or ASR would be a plus * Very good communication skills in English * Good command of French would be a plus but is not mandatory *SCIENTIFIC ENVIRONMENT* The thesis will be conducted within the Getalp teams of the LIG laboratory (_https://lig-getalp.imag.fr/_ <https://lig-getalp.imag.fr/>) and the LIA laboratory (https://lia.univ-avignon.fr/). The GETALP team and the LIA have a strong expertise and track record in Natural Language Processing and speech processing. The recruited person will be welcomed within the teams which offer a stimulating, multinational and pleasant working environment. The means to carry out the PhD will be providedboth in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and LIA. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments. The PhD position will be co-supervised by Mickael Rouvier (LIA, Avignon) and Benjamin Lecouteux and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited and the partners from LIA, LIG and Dauphine Université PSL, Paris. Furthermore, the project will involve one of the founders of SpeechBrain, Titouan Parcollet with whom the candidate will interact closely. *INSTRUCTIONS FOR APPLYING* Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Mickael Rouvier (_mickael.rouvier(a)univ-avignon.fr_ <mailto:mickael.rouvier@univ-avignon.fr>), Benjamin Lecouteux(benjamin.lecouteux(a)univ-grenoble-alpes.fr) and François Portet (_francois.Portet(a)imag.fr_ <mailto:francois.Portet@imag.fr>). We celebrate diversity and are committed to creating an inclusive environment for all employees. *REFERENCES:* [Noé2021] Noé, P.- G., Mohammadamini, M., Matrouf, D., Parcollet, T., Nautsch, A. & Bonastre, J.- F. Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation in Proc. Interspeech 2021 (2021), 1902–1906. [Sari2021] Sarı, L., Hasegawa-Johnson, M. & Yoo, C. D. Counterfactually Fair Automatic Speech Recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 3515–3525 (2021) [Yue2021] Yue, X. & Li, H. Phonetically Motivated Self-Supervised Speech Representation Learning in Proc. Interspeech 2021 (2021), 746–750. [Zaiem2022] Zaiem, S., Parcollet, T. & Essid, S. Pretext Tasks Selection for Multitask Self-Supervised Speech Representation in AAAI, The 2nd Workshop on Self-supervised Learning for Audio and Speech Processing, 2023 (2022). [Zhao2017] Zhao, J., Wang, T., Yatskar, M., Ordonez, V. & Chang, K. - W. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017), 2979–2989. -- François PORTET Professeur - Univ Grenoble Alpes Laboratoire d'Informatique de Grenoble - Équipe GETALP Bâtiment IMAG - Office 333 700 avenue Centrale Domaine Universitaire - 38401 St Martin d'Hères FRANCE Phone: +33 (0)4 57 42 15 44 Email:francois.portet@imag.fr www:http://membres-liglab.imag.fr/portet/

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Grenoble Interdisciplinary Institute in Artificial Intelligence : Call for Research Fellow Chairs 2023
by François Portet 22 May '23

22 May '23

*Call for Research Fellow Chairs 2023* MIAI, the Grenoble Interdisciplinary Institute in Artificial Intelligence (https://miai.univ-grenoble-alpes.fr/), is opening three research fellow chairs in AI reserved to persons who have spent most of their research career outside France (see below). MIAI is one of the four AI institutes created by the French government and is dedicated to AI for the human beings and the environment. Research activities in MIAI aim to cover all aspects of AI and applications of AI with a current focus on embedded and hardware architectures for AI, learning and reasoning, perception and interaction, AI & society, AI for health, AI for environment & energy, and AI for industry 4.0. These research fellow chairs aim to to address important and ambitious research problems in AI-related fields and will partly pave the way for the future research to be conducted in MIAI. Successful candidates will be appointed by MIAI and will be allocated, for the whole duration of the chair, a budget of 250k€ covering PhD and/or postdoc salaries, internships, travels, … They will be part of MIAI and the French network of AI institutes (comprising, in addition to MIAI, the AI institutes in Paris, Toulouse and Nice) which provide a very dynamic environment for conducting research in AI. *Eligibility*//To be eligible, candidates must hold a PhD from a non-French university obtained after January 2014 for male applicants and after 2014-/n/, where /n/ is the number of children, for female applicants. They must also have spent more than two thirds of their research career since the beginning of their PhD outside France. Lastly, they should be pursuing internationally recognized research in AI-related fields (including applications of AI to any research field). *To apply* Interested candidates should first contact Eric Gaussier (eric.gaussier(a)univ-grenoble-alpes.fr) to discuss salary and application modalities. It is important to note that candidates should identify a local collaborator working in one of the Grenoble academic research labs with whom they will interact. If selected, they will join the research team of this collaborator. They should then send their application to Manel Boumegoura (manel.boumegoura(a)univ-grenoble-alpes.fr) and Eric Gaussier (eric.gaussier(a)univ-grenoble-alpes.fr) /by March 11, 2023/. Each application should comprise a 2-page CV, a complete list of publications, 2 reference letters, a letter from the local collaborator indicating the relevance and importance of the proposed project, and a 4-page description of the research project which can target any topic of AI or applications of AI. It is important to emphasize, in the description, the ambition, the originality and the potential impact of the research to be conducted, as well as the collaborations the candidate has or will develop with Grenoble researchers in order to achieve her or his research goals. *Starting date and duration* Each chair is intended for 3 to 4 years, starting no later than September 2023. *Location* The work will take place in Grenoble, in the research lab of the identified collaborator. For any question, please contact Eric Gaussier (eric.gaussier(a)univ-grenoble-alpes.fr) or Manel Boumegoura (manel.boumegoura(a)univ-grenoble-alpes.fr). *******

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eLex 2023: Invisible Lexicography - Call for papers
by Miloš Jakubíček 10 May '23

10 May '23

(apologies for multiple postings) *CALL FOR PAPERS* <https://elex.link/elex2023/call-for-papers/> *eLex 2023: Electronic lexicography in the 21st century.* The topic of next year's conference is Invisible Lexicography. Dates: 27-29 June 2023 (with workshops on June 26th) Venue: Hotel Passage, Brno, Czechia Deadline for abstract submissions: January 31st 2023 Conference website: https://elex.link/elex2023/ Language of the conference: English Format: The conference will be organized as a hybrid event and while we encourage everyone to participate on-site, we plan to provide live streaming and recording of the event for registered participants. Looking forward to seeing you all in Brno, Miloš Jakubíček in the name of the organising committee

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Digital lexicography and lexical computing workshop, Cambridge, UK
by Ondřej Matuška 02 May '23

02 May '23

*<Lexicom/>* a workshop in digital lexicography and lexical computing *SAVE THE DATE* *Jesus College, Cambridge, UK*11 – 15 September 2023 Your 5 days to get up-to-date with the latest developments in *corpus-driven lexicography* and to activate and enhance your *corpus building and corpus query skills* with some of the top experts in the field. For the programme, lecturers, invited speakers, fees and registration, visit this website *lexicom.courses <https://lexicom.courses/lexicom-2023-cambridge-uk-lexicography-workshop/>* I hope to meet you in Cambridge in September! Ondřej *Ondřej Matuška* sketchengine.co.uk <http://www.sketchengine.co.uk> | Facebook <https://www.facebook.com/SketchEngine/> | LinkedIn <https://www.linkedin.com/in/ondrejmatuska> | Twitter <https://twitter.com/SketchEngine>

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CD-MAKE 2023 special session on “Multi-Perspectivist Data and Learning”
by Valerio Basile 08 Mar '23

08 Mar '23

Dear colleagues, We are happy to announce the call for papers for our one-day special session on “Multi-Perspectivist Data and Learning 2023” at the upcoming CD MAKE 2023 conference: https://cd-make.net/special-sessions/multi-perspectivist-data-and-learning/ *************************************************** Description, scope and aims Many Artificial Intelligence applications are based on supervised machine learning (ML), which ultimately grounds on manually annotated data. The annotation process (i.e., ground-truthing) is often performed in terms of a majority vote and this has been proved to be often problematic, as highlighted by recent studies on the evaluation of ML models. Recently, a different paradigm for ground-truthing has started to emerge, called data perspectivism, which moves away from traditional majority aggregated datasets, towards the adoption of methods that integrate different opinions and perspectives within the knowledge representation, training, and evaluation steps of ML processes, by adopting a non-aggregation policy. This alternative paradigm obviously implies a radical change in how we develop and evaluate ML systems: such ML systems have to take into account multiple, uncertain, and potentially mutually conflicting views. This obviously brings both opportunities and difficulties: novel models or training techniques may need to be designed, and the validation phase may become more complex. Nonetheless, initial works have shown that data perspectivism can lead to better performances, and could also have important implications in terms of human-in-the-loop and interpretable AI, as well as in regard to the ethical issues or concerns related to the use of AI systems. The scope of this special session is to attract contributions related to the management of subjective, crowd-sourced, multi-perspective, or otherwise non-aggregated data in ground-truthing, machine learning, and more generally artificial intelligence systems. Invited contributions: full research papers and research in progress papers. *************************************************** Topics of interest: - Subjective, uncertain, or conflicting information in annotation and crowdsourcing processes; - Limits and problems with standard data annotation and aggregation processes; - Theoretical studies on the problem of learning from multi-rater and non-aggregated data; - Participation mechanisms/incentives/gamification for rater engagement and crowdsourcing; - Ethical and legal concerns related to annotation and aggregation processes in ground-truthing; - Creation and documentation of multi-rater and non-aggregated datasets and benchmarks; - Development of ML algorithms for multi-rater and non-aggregated data; - Techniques for the evaluation of ML systems based on multi-rater and non-aggregated data; - Applications of data perspectivism and non-aggregated data to eXplainable AI, human-in-the-loop AI and algorithmic fairness; - Experimental and application studies of ML/AI systems on multi-rater and non-aggregated data, in possibly different application domains (e.g. NLP, medicine, legal studies, etc.) *************************************************** Important dates: Submission Deadline March 27, 2023 (AoE) Author Notification June 01, 2023 Proceedings Version June 22, 2023 (AoE) Conference August 29 – September 01, 2023 *************************************************** Special Session Chairs: Federico Cabitza (University of Milano-Bicocca, Italy) Andrea Campagner (University of Milano-Bicocca, Italy) Valerio Basile (University of Turin, Italy) Program Committee (provisional): Nahuel Costa Cortez, University of Oviedo Elisa Leonardelli, Fondazione Bruno Kessler (FBK) Julian Lienen, Paderborn University Gavin Abercrombie, Heriot-Watt University Simona Frenda, University of Turin Marília Barandas, Fraunhofer Portugal AICOS Duarte Folgado, Fraunhofer Portugal AICOS Barbara Plank, Ludwig Maximilian University of Munich Tommaso Caselli, Rijksuniversiteit Groningen *************************************************** Related readings [1] Cabitza, F., Campagner, A., Basile, V. (2023) Toward a Perspectivist Turn in Ground Truthing for Predictive Computing Proceedings of the AAAI Conference on Artificial Intelligence (extended preprint at: https://arxiv.org/pdf/2109.04270.pdf) [2] V. Basile (2020) It’s the End of the Gold Standard as we Know it. On the Impact of Pre-aggregation on the Evaluation of Highly Subjective Tasks Proceedings of the AIxIA 2020 Discussion Papers Workshop [3] F. Cabitza, A. Campagner, L. M. Sconfienza (2020) As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI BMC Medical Informatics and Decision Making [4] Plank, B. (2022). The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation. arXiv preprint arXiv:2211.02570.

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3-year PhD position in Computational Models of Semantic Memory and its Acquisition (Inria and University of Lille, France)
by Pascal Denis 28 Feb '23

28 Feb '23

Hello, Could you please distribute the following job offer? Thanks. Best, Pascal ------------------------------------------------------------------------------------- 3-year PhD position in Computational Models of Semantic Memory and its Acquisition (Inria and University of Lille, France) We invite applications for a 3-year PhD position at the University of Lille in the context of the recently funded research project "COMANCHE" (Computational Models of Lexical Meaning and Change). The position is funded by Inria, the French national research institute in Computer Science and Applied Mathematics. COMANCHE proposes to transfer and adapt neural word embeddings algorithms to model the acquisition and evolution of word meaning, by comparing them with linguistic theories on language acquisition and language evolution. At the intersection between Natural Language Processing, psycholinguistics and historical linguistics, this project intends to validate or revise some of these theories, while also developing computational models that are less data hungry and computationally intensive as they exploit new inductive biases inspired by these disciplines. The first strand of the project, on which the successful candidate will work, focuses on the development of computational models of semantic memory and its acquisition. Two main research directions will be pursued. On the one hand, we will compare the structural properties associated to different semantic spaces derived from word embedding algorithms to those found in human semantic memory as reflected in behavioral data (such as typicality norms) as well as brain imaging data. The latter data will then used as additional supervision to inject more hierarchical structure into the learned semantic spaces. One the other hand, we intend to experiment with training regimes for word embedding algorithms that are closer to those of humans when they acquire language, controlling the quantity as well as the linguistic complexity of the inputs fed to the learning algorithms through the use of longitudinal and child directed speech corpora (e.g., CHILDES, Colaje). In both cases, both English and French data will be considered. The successful candidate holds a Master's degree in computational linguistics or computer science or cognitive science and has prior experience in word embedding models. Furthermore, the candidate will provide strong programming skills, expertise in machine learning approaches and is eager to work across languages. The position is affiliated with the MAGNET team at Inria, Lille [1] as well as with the SCALAB group at University of Lille [2] in an effort to strenghten collaborations between these two groups, and ultimately foster cross-fertilizations between Natural Language Processing and Psycholinguistics. Applications will be considered until the position is filled. However, you are encouraged to apply early as we shall start processing the applications as and when they are received. Applications, written in English or French, should include a brief cover letter with research interests and vision, a CV (including your contact address, work experience, publications), and contact information for at least 2 referees. Applications (and questions) should be sent to Angèle Brunellière (angele.brunelliere(a)univ-lille.fr) and Pascal Denis (pascal.denis(a)inria.fr). The starting date of the position is 1 October 2022 or soon thereafter, for a total of 3 full years. Best regards, Angèle Brunellière and Pascal Denis [1] https://team.inria.fr/magnet/ [2] https://scalab.univ-lille.fr/ -- Pascal ---- Pour une évaluation indépendante, transparente et rigoureuse ! Je soutiens la Commission d'Évaluation de l'Inria. ---- +++++++++++++++++++++++++++++++++++++++++++++++ Pascal Denis Equipe MAGNET, INRIA Lille Nord Europe Bâtiment B, Avenue Heloïse Parc scientifique de la Haute Borne 59650 Villeneuve d'Ascq Tel: ++33 3 59 35 87 24 Url: http://researchers.lille.inria.fr/~pdenis/ +++++++++++++++++++++++++++++++++++++++++++++++

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[Cfp] Contemporary Trends in English-Language Studies
by Leszek Szymański 21 Feb '23

21 Feb '23

Dear Colleagues, The Institute of Modern Languages at the University of Zielona Góra, Poland, announces a conference titled "Contemporary Trends in English-Language Studies". This year's edition will be held entirely online on May 18-19. More information is available at: https://sites.google.com/view/ctiels/ Thank you! Leszek

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2024

2023

2022

Corpora January 2023