Il giorno ven 12 ago 2022 alle 14:00 <[email protected]> ha scritto:
> Send Corpora mailing list submissions to > [email protected] > > To subscribe or unsubscribe via email, send a message with subject or > body 'help' to > [email protected] > > You can reach the person managing the list at > [email protected] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Corpora digest..." > > Today's Topics: > > 1. [CfP] TREC Health Misinformation Track 2022 (Maria Maistro) > 2. [CfP] ACM TOIS Efficiency in Neural IR (Maria Maistro) > 3. Call for Badges - ACM SIGIR Artifact Badges Continuous Submission > (Nicola Ferro) > 4. Call for proposals: Natural Language Processing (John Benjamin’s) > (Caro) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Fri, 12 Aug 2022 08:03:18 +0000 > From: Maria Maistro <[email protected]> > Subject: [Corpora-List] [CfP] TREC Health Misinformation Track 2022 > To: "[email protected]" <[email protected]> > Message-ID: <[email protected]> > Content-Type: multipart/alternative; > boundary="_000_86B5F7089063456AB790888B9639E00Fkudk_" > > Call for Participation - TREC Health Misinformation Track 2022 > https://trec-health-misinfo.github.io > > Overview 🧐 > -------------------------- > Web search engines are frequently used to help people make decisions about > health-related issues. Unfortunately, the web is filled with misinformation > regarding the efficacy of treatments for health issues. Search users may > not be able to discern correct from incorrect information, nor credible > from non-credible sources. As a result of finding misinformation deemed by > the user to be useful to their decision making task, they can make > incorrect decisions that waste money and put their health at risk. > > The TREC Health Misinformation track fosters research on retrieval methods > that promote reliable and correct information over misinformation for > health-related decision making tasks. > > Tasks 💼 > -------------------------- > * Ad-hoc Retrieval Task: design a ranking model that promotes credible and > correct information over incorrect information; > * Answer Prediction Task: predict the answer to the topic’s stance. > > Guidelines 📋 we u guy > -------------------------- > * Corpus: noclean version of the C4 dataset ( > https://huggingface.co/datasets/allenai/c4); > * Topics: about consumer health search (people seeking health advice > online); > * Runs: runs may be either automatic or manual with the standard TREC run > format. > > Detailed guidelines: https://trec-health-misinfo.github.io > > Important Dates 🔥 > -------------------------- > * Runs due from participants: August 28, 2022 > * Evaluation results returned: End of September 2022 > * Notebook paper due: October 2022 > * TREC 2022 Conference: November 14-18, 2022 > * Final paper due: February 2023 > > Organization 👔 > -------------------------- > * Charles Clarke, University of Waterloo > * Maria Maistro, University of Copenhagen > * Mark Smucker, University of Waterloo > > > ——— > > Maria Maistro, PhD > Tenure-track Assistant Professor > Department of Computer Science > University of Copenhagen > Universitetsparken 5, 2100 Copenhagen, Denmark > -------------- next part -------------- > A message part incompatible with plain text digests has been removed ... > Name: not available > Type: text/html > Size: 3101 bytes > Desc: not available > > ------------------------------ > > Message: 2 > Date: Fri, 12 Aug 2022 08:07:34 +0000 > From: Maria Maistro <[email protected]> > Subject: [Corpora-List] [CfP] ACM TOIS Efficiency in Neural IR > To: "[email protected]" <[email protected]> > Message-ID: <[email protected]> > Content-Type: multipart/alternative; > boundary="_000_3D36AF0DFA6D4A4FBE65DF108B703AD2kudk_" > > Call for Papers - ACM Transactions on Information Systems > Special Section on Efficiency in Neural Information Retrieval > > Full Call of Papers: https://dl.acm.org/journal/tois/calls-for-papers > > Overview 🧐 > -------------------------- > The aim of this Special Section is to engage with researchers in > Information Retrieval, Natural Language Processing, and related areas and > gather insight into the core challenges in measuring, reporting, and > optimizing all facets of efficiency in Neural Information Retrieval (NIR) > systems, including time-, space-, resource-, sample- and energy- > efficiency, among other factors. > This special section solicits perspectives from active researchers to > advance our understanding of and to overcome efficiency challenges in NIR. > In particular, researchers are encouraged to examine the ever-growing > model complexity through appropriate empirical analysis, to propose models > that require less data, computational resources, and energy for training > and fine-tuning with similarly efficient inference, to ask if there are > meaningful simplifications of the existing training processes or model > architectures that lead to comparable quality, and explore a multi-faceted > evaluation of NIR models from quality to all dimensions of efficiency with > standardized metrics. > > Topics 🔍 > -------------------------- > We welcome submissions on the following topics, including but not limited > to: > * Novel NIR models that reach competitive quality but are designed to > provide efficient training or > inference; > * Efficient NIR models for decentralized IR tasks such as conversational > search; > * Efficient NIR models for IR-related tasks such as question answering and > recommender systems; > * Efficient NIR for resource-constrained devices; > * Scalability of NIR systems; > * Efficient NIR for text and cross-modal search; > * Strategies to optimize training or inference of existing NIR models; > * Sample-efficient training of NIR models; > * Efficiency-driven distillation, pruning, quantization, retraining, and > transfer learning; > * Empirical investigation of the complexity of existing NIR models through > an analysis of quality, interpretability, robustness, and environmental > impact; > * Evaluation protocols for efficiency in NIR. > > Important Dates 🔥 > -------------------------- > * Open for Submissions: Aug 1, 2022 > * Submissions deadline: Dec 31, 2022 > * First-round review decisions: Mar 31, 2023 > * Deadline for minor revision submissions: Apr 30, 2023 > * Deadline for major revision submissions: Jun 30, 2023 > * Notification of final decisions: Jul 31, 2023 > * Tentative publication: 2023 > > Guest Editors 📚 > -------------------------- > * Dr. Sebastian Bruch, Pinecone, United States of America > * Prof. Claudio Lucchese, Ca' Foscari University of Venice, Italy > * Dr. Maria Maistro, University of Copenhagen, Denmark > * Dr. Franco Maria Nardini, ISTI-CNR, Italy > > > ——— > Maria Maistro, PhD > Tenure-track Assistant Professor > Department of Computer Science > University of Copenhagen > Universitetsparken 5, 2100 Copenhagen, Denmark > -------------- next part -------------- > A message part incompatible with plain text digests has been removed ... > Name: not available > Type: text/html > Size: 4100 bytes > Desc: not available > > ------------------------------ > > Message: 3 > Date: Fri, 12 Aug 2022 10:55:54 +0200 > From: Nicola Ferro <[email protected]> > Subject: [Corpora-List] Call for Badges - ACM SIGIR Artifact Badges > Continuous Submission > To: [email protected] > Message-ID: <[email protected]> > Content-Type: multipart/alternative; > boundary="Apple-Mail=_92A380A6-3258-4B5B-A5DD-8556CDC7BDE2" > > ## ACM SIGIR Artifact Badges ## > > The ACM Special Interest Group on Information Retrieval (SIGIR) adheres to > and implements the ACM policies for "Artifact Review and Badging” ( > https://www.acm.org/publications/policies/artifact-review-and-badging-current > < > https://www.acm.org/publications/policies/artifact-review-and-badging-current>). > > > Artifact badging is not only intended for further improving our > experimental practices, but especially to highlight and recognize the > outstanding efforts made by those who go the extra mile to make their > experiments’ code and data not only available online, but also easy to use, > fully functional, and reproducible. > > Overall, the initiative promotes reproducibility of research results and > allows scientists and practitioners to immediately benefit from > state-of-the-art research results, without spending months re-implementing > the proposed algorithms and trying to find the right parameter values, or > creating datasets or running intensive user-oriented evaluation. We also > hope that it will indirectly foster scientific progress, since it allows > researchers to reliably compare with and build upon existing techniques, > knowing that they are using exactly the same implementation. > > Badge Types: > > ** Artifacts Evaluated – Functional ** The artifacts associated with the > research are found to be documented, consistent, complete, exercisable, and > include appropriate evidence of verification and validation. > > ** Artifacts Evaluated – Reusable and Available ** The artifacts > associated with the paper are of a quality that significantly exceeds > minimal functionality. That is, they have all the qualities of the > Artifacts Evaluated – Functional level, but, in addition, they are very > carefully documented and well-structured to the extent that reuse and > repurposing are facilitated. In particular, the norms and standards of the > research community for artifacts of this type are strictly adhered to. This > badge is applied to papers in which associated artifacts have been made > permanently available for retrieval. > > ** Results Reproduced ** The main results of the paper have been obtained > in a subsequent study by a person or team other than the authors, using, in > part, artifacts provided by the author. > > ** Results Replicated ** The main results of the paper have been > independently obtained in a subsequent study by a person or team other than > the authors, without the use of author-supplied artifacts > > The different types of ACM stamps are not meant to be a measure of the > scientific quality of the paper themselves or of the usefulness of > presented algorithms, which are assessed by means of the traditional > peer-review processes and by adoption/impact in the research and industry > community. Rather, they are a recognition of the service provided by > authors to the community by releasing the code and/or data and they are an > endorsement of the replicability and/or reproducibility of the results > presented in the paper. The stamps also alert users of the ACM Digital > Library about the presence and location of these artifacts in the ACM DL: > > Datasets – https://dl.acm.org/artifacts/dataset < > https://dl.acm.org/artifacts/dataset> > Software – https://dl.acm.org/artifacts/software < > https://dl.acm.org/artifacts/software> > > In this way, each artifact will be assigned its own DOI, will be directly > citable, and will be linked to its corresponding paper. > > > ## Artifact Submission ## > > ACM SIGIR Artifact Badges applies to artifacts complementing papers > accepted in one of the following venues: > > ACM Transactions on Information Systems (TOIS) > Annual International ACM SIGIR Conference on Research and Development in > Information Retrieval (SIGIR) > ACM on Conference on Human Information Interaction and Retrieval (CHIIR) > ACM SIGIR International Conference on the Theory of Information Retrieval > (ICTIR) > > The submission is always open and authors are welcome to apply for badges > as soon as their papers get accepted in one of the above venues. > > Irrespective of the nature of the artifacts, authors should create a > single Web page (whether on their site or a third-party repository service) > that contains the artifact, the paper, and all necessary instructions. > > For artifacts where this would be appropriate, it would be helpful to also > provide a self-contained bundle (including instructions) as a single file > (.tgz or .zip) for convenient offline use. > > The artifact submission thus consists of just the URL and any credentials > required to access the files submitted into the submission system: > > https://openreview.net/group?id=ACM.org/SIGIR/Badging < > https://openreview.net/group?id=ACM.org/SIGIR/Badging> > > > ## Artifact Preparation Guidelines and Review Procedure ## > > You can find more information about the ACM SIGIR Artifact Badges at: > > https://sigir.org/general-information/acm-sigir-artifact-badging/ < > https://sigir.org/general-information/acm-sigir-artifact-badging/> > > There you can also find detailed instructions and suggestions about how to > prepare your artifacts for each type of badge and the reviewing criteria > for each of them. > > Each artifact will be reviewed by a senior and junior member of the > Artifact Evaluation Committee (AEC). > > For any questions or clarifications, please contact us at: > > [email protected] <mailto:[email protected]> > > > ## ACM SIGIR Artifact Evaluation Committee (AEC) ## > > Chair and Vice-chair > Nicola Ferro, University of Padua, Italy [chair] > Johanne Trippas, RMIT University , Australia [vice-chair] > > Senior Members > Alessandro Benedetti, Sease, UK > Rob Capra, University of North Carolina at Chapel Hill, USA > Diego Ceccarelli, Bloomberg, UK > Anita Crescenzi, University of North Carolina at Chapel Hill, USA > Charles L . A. Clarke, University of Waterloo, Canada > Yi Fang, Santa Clara University, USA > Norbert Fuhr, University of Duisburg-Essen, Germany > Claudia Hauff, Delft University of Technology, The Netherlands > Jiqun Liu, University of Oklahoma, USA > Maria Maistro, University of Copenhagen, Denmark > Miguel Martinez, Signal AI, UK > Parth Mehta, Parmonic, USA > Martin Potthast, Leipzig University, Germany > Tetsuya Sakai, Waseda University, Japan > Ian Soboroff, National Institute of Standards and Technology (NIST), USA > Paul Thomas, Microsoft, Australia > Andrew Trotman, University of Otago, New Zealand > Min Zhang, Tsinghua University, China > > Junior Members > Valeriia Baranova, RMIT University, Australia > Arthur Barbosa Câmara, Delft University of Technology, The Netherlands > Hamed Bonab, University of Massachusetts Amherst, USA > Kathy Brennan, Google, USA > Timo Breuer, TH Köln, Germany > Guglielmo Faggioli, University of Padua, Italy > Alexander Frummet, University of Regensburg, Germany > Darío Garigliotti, Aalborg University, Denmark > Chris Kamphuis, Radboud University, The Netherlands > Johannes Kiesel, Bauhaus-Universität Weimar, Germany > Yuan Li, University of North Carolina at Chapel Hill, USA > Joel Mackenzie, University of Melbourne, Australia > Antonio Mallia, New York University, USA > David Maxwell, Delft University of Technology, The Netherlands > Felipe Moraes, Delft University of Technology, The Netherlands > Ahmed Mourad, University of Queensland, Australia > Zuzana Pinkosova, University of Strathclyde, UK > Chen Qu, University of Massachusetts Amherst, USA > Anna Ruggero, Sease, UK > Svitlana Vakulenko, University of Amsterdam, The Netherlands > Sasha Vtyurina, KIRA systems, Canada > Oleg Zendel, RMIT University, Australia > Steven Zimmerman, University of Essex, UK > > > -------------- next part -------------- > A message part incompatible with plain text digests has been removed ... > Name: not available > Type: text/html > Size: 10601 bytes > Desc: not available > > ------------------------------ > > Message: 4 > Date: Fri, 12 Aug 2022 09:11:31 +0000 > From: Caro Quintana, Rocío I. <[email protected]> > Subject: [Corpora-List] Call for proposals: Natural Language > Processing (John Benjamin’s) > To: "[email protected]" <[email protected]> > Message-ID: <[email protected] > P265.PROD.OUTLOOK.COM> > Content-Type: multipart/alternative; boundary="_000_CWXP265MB3831A7 > 312B9578FE45975DC196679CWXP265MB3831GBRP_" > > > Natural Language Processing (John Benjamin’s) > > Call for Book Proposals > > > John Benjamins' NATURAL LANGUAGE PROCESSING Book Series invites new book > proposals to respond to the growing demand for Natural Language processing > (NLP) literature. Three general types of books are considered for > publication: > > Monographs - featuring (i) original leading cutting-edge research (the > monograph could be based on an outstanding PhD thesis), or (ii) surveys of > the state-of-the art of specific NLP tasks or applications. > > Collections – (i) books focusing on a particular NLP area (e.g. emerging > from successful NLP workshops or as a result of editors’ calls for papers) > or (ii) books which include papers covering a wide range of topics (e.g. > emerging from competitive NLP conferences or as a result of proposals for > books of the type “Reading In NLP”). > > Course books – (i) general NLP course books or (ii) course books on a > particular key area of NLP (e.g. Speech Processing, Computational > Syntax/Parsing). > > Authors will be encouraged to append supplementary materials such as > demonstration programs, NLP software, corpora etc. and to indicate > websites, computational language resources etc. where appropriate. > > This call invites proposals from potential authors of the types of books > described above. Proposals on any topic related to Natural Language > Processing are welcome. > > Topics > > The scope of the series is comprehensive ranging from theoretical > Computational Linguistics topics (Computational Syntax, Computational > Semantics etc.) to highly practical Language Technology topics (speech > recognition, information extraction, information retrieval etc.). The > series covers both written language and speech; it welcomes works covering > (but not limited to) areas such as: phonology, morphology, syntax, > semantics, discourse, pragmatics, dialogue, text understanding and > generation, machine translation, machine-aided translation, translation > aids and tools, corpus-based language processing; written and spoken > natural language interfaces, knowledge acquisition, information > extraction, text summarisation, text classification, computer-aided > language learning, language resources. > > New results in NLP based on modern alternative theories and methodologies > as opposed to the mainstream techniques of symbolic NLP such as > analogy-based, statistical, connections as well as hybrid and multimedia > approaches, will be also welcome. > > Advisory board > > The new series’ editor is Ruslan Mitkov (University of Wolverhampton) and > the advisory board of the series includes: > > - Eduardo Blanco (University of North Texas) > - Gloria Corpas (University of Malaga) > - Robert Dale (Macquarie University) > - Elizaveta Goncharova (National Research University) > - Veronique Hoste (Veronique Hoste) > - Eduard Hovy (Carnegie Mellon University) > - Lori Lamel (The Computer Sciences Laboratory for Mechanics and > Engineering Sciences) > - Carlos Martín-Vide (Rovira i Virgili University) > - Johanna Monti (University of Naples "L'Orientale" ) > - Roberto Navigli (Sapienza University of Rome) > - Nicolas Nicolov (AI/ML, Avalara Inc.) > - Constantin Orasan (University of Surrey) > - Paolo Rosso (Universitat Politècnica de València) > - Raheem Sarwar (University of Wolverhampton) > - Khalil Sima’an (University of Amsterdam) > - Richard Sproat (Google Research) > - Key-Yih Su (Institute of Information Science, Academia Sinica) > > The managing editor at John Benjamins is Kees Vaes (Email > [email protected]). > > Submission of proposals > > Interested authors should submit proposals by email (plain text or pdf > files) to the series editor: > Prof. Dr. Ruslan Mitkov > Email [email protected]<mailto:[email protected]> > with a copy to Ms Rocío Caro Quintana ([email protected]<mailto: > [email protected]>), Editorial Assistant. > > The proposals should include an outline of the book (1-2 pages), a > preliminary table of contents, the target readership, related publications, > how the book will differ from other similar books in the area (if > applicable), time-scale and information about the prospective author > (relevant experience in the field, publications etc.). > > Each proposal will be reviewed by members of the advisory board or > additional reviewers. > > More information > > More information at Natural Language Processing (benjamins.com)< > https://benjamins.com/catalog/nlp>. > > > -------------- next part -------------- > A message part incompatible with plain text digests has been removed ... > Name: not available > Type: text/html > Size: 17268 bytes > Desc: not available > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > Corpora mailing list -- [email protected] > To unsubscribe send an email to [email protected] > > > ------------------------------ > > End of Corpora Digest, Vol 219, Issue 1 > *************************************** > -- R Tr
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