- Corpora - ELRA lists

[Final call] Four fully-funded 5-year PhD positions at University of Gothenburg, Sweden
by Peter Ljunglöf 14 Aug '23

14 Aug '23

[Note: the application deadline is Sunday 20 August] The University of Gothenburg, Sweden, is offering four fully-funded PhD positions in computer science and engineering where the candidates can choose the project themselves out of fourteen options. Two of the projects are related to NLP, one about efficient algorithms for corpus searching, and another about automatic generation of Wikipedia articles. See the ad for more information: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89… The positions are fully funded for 5 years, including 20% teaching or other departemental duties. Application deadline: 20 August 2023 best regards, Peter Ljunglöf ------- ------ ----- ---- --- -- - - - - - peter ljunglöf peter.ljunglof(a)gu.se data- och informationsteknik, och språkbanken göteborgs universitet och chalmers tekniska högskola -------------- --------- -------- ------- ------ ----- ---- --- -- - - - - -

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Fully-funded PhD position “Computer Science: Knowledge Graphs & Natural Language Processing” (m/f/d), Leibniz University Hannover, Germany
by Jennifer D'Souza 14 Aug '23

14 Aug '23

Dear colleagues and friends, The Research Center L3S invites applications for the position of a Research Associate / PhD Candidate (m/f/d) “Computer Science: Knowledge Graphs & Natural Language Processing” (Salary Scale 13 TV-L; 100 %) starting at the earliest possible date. The position is limited to 3 years with the possibility of extension. The regular weekly working time is 39.8 hours (full-time). *Description:* The PhD topic will be in the context of the HybrInt project and the Open Research Knowledge Graph (https://orkg.org/) focusing on building knowledge graphs for the agricultural domain. The aim of these projects is to research and develop NLP solutions as large language models (LLMs) for crowdsourcing, representing and managing semantically structured, rich representations of scholarly contributions and research data in knowledge graphs and thus develop a novel model for scholarly communication. In the context of the PhD thesis you will be responsible for building and maintaining the ORKG data ingestion and processing pipelines to ensure the flow of high-quality semantified resources from academic publications. Your main responsibility in this position will be to build scalable solutions that crawl, ingest, process publications, and thereby enrich the ORKG. You will work alongside the ORKG engineering team to setup the AI/NLP ecosphere. The tasks will focus on: * Working in the areas of Natural Language Processing (text mining, information extraction, information retrieval/search) and Machine Learning of scholarly communication media (digital) data * Research and development of suitable Large Language Models (LLMs) as NLP solutions * Conceptually designing, modeling, and implementing data-driven services in one or more areas of information retrieval and extraction, data enrichment, and linking of data *Application Deadline:* 15.09.2023 *Web Address for Applications:* https://www.uni-hannover.de/en/jobs/id/6435/ (en); https://www.uni-hannover.de/de/jobs/id/6435/ (de) Yours cordially, Jennifer

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Release of the AIGC corpus - The aiTECCL Corpus
by Xu, Jiajin 12 Aug '23

12 Aug '23

Dear list members, We are excited to announce that the AIGC corpus - The aiTECCL Corpus, is now available to all members of the research community. The aiTECCL Corpus was compiled by Jiajin Xu and Mingchen Sun of the Corpus Research Group of the National Research Centre for Foreign Language Education at Beijing Foreign Studies University. The corpus consisting of two million words generated by the GPT-3.5 model, using identical writing prompts to those employed in *the TECCL Corpus* <http://corpus.bfsu.edu.cn/info/1070/1449.htm>, aims to serve as a reference corpus that exhibits a native-like linguistic quality. The corpus is made available online on 9 August, 2023. URL: http://114.251.154.212/cqp/ Username: test Password: test Please cite: Xu, Jiajin & Mingchen Sun. 2023. aiTECCL: An AIGC English Essay Corpus. Beijing: National Research Centre for Foreign Language Education, Beijing Foreign Studies University. Available online: http://corpus.bfsu.edu.cn/info/1082/1913.htm *Justifying the concept of "AIGC Corpus" (Artificial Intelligence Generated Content Corpus) or Generative Corpus* The creation of the AIGC Corpus helps expand the concept of "corpus". In the classic definition of a corpus, the included materials must be language samples that are authentically or naturally occurring in real-life communication. Clearly, generative texts do not fall under this category. We believe that the rationale for the generative corpus can be viewed from at least three aspects: 1. The so-called principle of "authenticity" itself is a matter of degree. For example, whether essays written by learners under exam conditions belong to genuine communication is questionable. In existing research, some elicited data also has authenticity issues similar to those found in learners' interlanguage. Therefore, from the perspective of existing corpora, there are texts with varying degrees of authenticity. 2. The generative corpus can serve as an essential complement to existing corpora. The emergence of the generative corpus can reconcile the distinction between "probable language" and "possible language." For linguistic instances that have not yet appeared in reality, they can be generated using large language models. 3. Creating a corpus using artificial intelligence technology is a second-to-best solution under the current conditions for building specific types of corpora. For example, the aiTECCL corpus simulates a reference corpus of approximately 10,000 essays, close to the English native speaker language quality, and written on the same topics as Chinese learners. Without the use of artificial intelligence methods for generation, it might be impossible to obtain a reference corpus of such quality and comparability. Similarly, for corpus construction of languages from least-developed countries or countries with extremely small populations, without generative technology, it would be impossible to establish in the short term. Further details about the prompt and the Python script we utilised to create the corpus will be provided on the site soon <http://corpus.bfsu.edu.cn/info/1082/1913.htm>. Best wishes, Jiajin Xu Ph.D., Professor National Research Centre for Foreign Language Education Beijing Foreign Studies University, China

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CFP: 2nd Shared Task on Indian Language Summarization (ILSUM 2023)
by Parth Mehta 12 Aug '23

12 Aug '23

Apologies for the multiple postings. ---- *Indian Language Summarization (ILSUM 2023)* Website: https://ilsum.github.io/ To be organized in conjunction with FIRE 2023 (fire.irsi.res.in) 15th-18th December 2023, Goa, India ------------------------------------------------------- The second shared task on Indian Language Summarization (ILSUM) aims at creating an evaluation benchmark dataset for Indian Languages. This year ILSUM consists of two subtasks Subtask 1: This task builds upon the task from ILSUM 2022. In the first edition, we covered two major Indian languages Hindi and Gujarati alongside Indian English, a widely recognized dialect of the English Language. This year's edition adds the Bengali language and an expanded dataset for the languages from last year. Further, we will provide abstractive summaries for a subset of each language (~1000 per language) apart from the headlines which are semi-extractive summaries in nature. Like the previous edition, this will be a classic summarization task, where we will provide ~15,000 article-summary pairs for each language and the participants are expected to generate a fixed-length summary. Subtask 2: The task is centred around identifying factual errors in machine-generated summaries. With the recent implosion of Large Language models, . While these LLMs are very good at summarization, among other NLP tasks, they are often prone to hallucinations. This means the model generates information that is not accurate, not based on its training data, or is completely made up but looks accurate and reliable. Further, such tools can be misused to generate misleading or outright incorrect information. Identifying such inaccuracies can be a challenging task. Through this subtask, we aim to address the problem of identifying factually incorrect information in LLM-generated summaries. Participants will be provided with an article and its corresponding machine-generated summary. The objective is to identify the presence of factual incorrectness in the summaries if any, and classify them in one of the predefined categories. *Tentative Timeline* ------------- 7st August - Training Data Released and Registrations open 10th September - Test Data Release 20th September - Run Submission Deadline 25th September - Results Declared 10th October - Working notes due 25th October - Reviews Due 30th October - Camera Ready Submissions due 15th-18th December - FIRE 2023 at Goa, India *Organisers* ---------------- Jagrat Patel, LDRP-ITR, Gandhinagar, India Jaivin Barot, LDRP-ITR, Gandhinagar, India Tanishka Gaur, LDRP-ITR, Gandhinagar, India Shrey Satapara, Indian Institute of Technology, Hyderabad, India Sandip Modha, LDRP-ITR, Gandhinagar, India Parth Mehta, Parmonic, USA Debasis Ganguly, University of Glasgow, Scotland *For regular updates subscribe to our mailing list: **ilsum(a)googlegroups.com**

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Second Call for Papers: The 10th Workshop on Argument Mining @ EMNLP 2023 December 7, 2023
by milad.alshomary＠uni-paderborn.de 11 Aug '23

11 Aug '23

SECOND CALL FOR PAPERS The 10th Workshop on Argument Mining @ EMNLP 2023 December 7, 2023 https://argmining-org.github.io/2023/ The 10th Workshop on Argument Mining will be held on December 7, 2023 in Singapore together with EMNLP 2023. This will be a hybrid event. The Workshop on Argument Mining provides a regular forum for the presentation and discussion of cutting-edge research in argument mining (a.k.a argumentation mining) to both academic and industry researchers. By continuing a series of nine successful previous workshops, this edition will welcome the submission of long, short, and demo papers. It will feature two shared tasks, a panel on the last ten years of Argument Mining, and a keynote talk. IMPORTANT DATES - Direct paper submission deadline (Softconf): September 1, 2023 - Paper commitment from ARR: September 15, 2023 - Notification of acceptance: October 7, 2023 - Camera-ready submission: October 18, 2023 - Workshop: December 7, 2023 TOPICS OF INTEREST The topics for submissions include but are not limited to: - Automatic identification of argument components (e.g., premises and conclusions), the structure in which they form an argument, and relations between arguments and counterarguments (e.g., support and attack) in as well as across documents - Automatic assessment of arguments and argumentation with respect to various properties, such as stance, clarity, and persuasiveness - Automatic generation of arguments and their components, including the consideration of discourse goals (e.g., stages of a critical discussion or rhetorical strategies) - Creation and evaluation of argument annotation schemes, relationships to linguistic and discourse annotations, (semi-) automatic argument annotation methods and tools, and creation of argumentation corpora - Argument mining in specific genres and domains (e.g., social media, education, law, and scientific writing), each with a unique style (e.g., short informal text, highly structured writing, and long-form documents) - Argument mining and generation from multi-modal and/or multilingual data - Integration of commonsense and domain knowledge into argumentation models for mining and generation - Combination of information retrieval methods with argument mining, e.g., in order to build the next generation of argumentative (web) search engines - Real-world applications, including argument web search, opinion analysis in customer reviews, argument analysis in meetings, misinformation detection - Perspectivist approaches to subjective argument mining tasks for which multiple ”ground truths” may exist, including multi-perspective machine learning and the creation of non-aggregated datasets - Reflection on the ethical aspects and societal impact of argument mining methods - Reflection on the future of argument mining in light of the fast advancement of large language models (LLMs). SUBMISSIONS The organizing committee welcomes the submission of long papers, short papers, and demo descriptions. Accepted papers will be presented either via oral or poster presentations. They will be included in the EMNLP proceedings as workshop papers. - Long paper submissions must describe substantial, original, completed, and unpublished work. Wherever appropriate, concrete evaluation and analysis should be included. Long papers must be no longer than eight pages, including title, text, figures and tables. An unlimited number of pages is allowed for references. Two additional pages are allowed for appendices, and an extra page is allowed in the final version to address reviewers’ comments. - Short paper submissions must describe original and unpublished work. Please note that a short paper is not a shortened long paper. Instead, short papers should have a point that can be made in a few pages, such as a small, focused contribution; a negative result; or an interesting application nugget. Short papers must be no longer than four pages, including title, text, figures and tables. An unlimited number of pages is allowed for references. One additional page is allowed for the appendix, and an extra page is allowed in the final version to address reviewers’ comments. - Demo descriptions must be no longer than four pages, including title, text, examples, figures, tables, and references. A separate one-page document should be provided to the workshop organizers for demo descriptions, specifying furniture and equipment needed for the demo. Multiple Submissions ArgMining 2023 will not consider any paper that is under review in a journal or another conference or workshop at the time of submission, and submitted papers must not be submitted elsewhere during the review period. ArgMining 2023 will also accept submissions of ARR-reviewed papers, provided that the ARR reviews and meta-reviews are available by the ARR commitment deadline (September 15). However, ArgMining 2023 will not accept direct submissions that are actively under review in ARR, or that overlap significantly (>25%) with such submissions. Submission Format All long, short, and demonstration submissions must follow the two-column EMNLP 2023 format. Authors are expected to use the LaTeX or Microsoft Word style template (https://2023.emnlp.org/calls/style-and-formatting/). Submissions must conform to the official EMNLP style guidelines, which are contained in these templates. Submissions must be electronic, in PDF format. Submission Link and Deadline For Direct Submissions Authors have to fill in the submission form in the START system and upload a PDF of their paper before September 1, 2023, 11:59 pm UTC-12h (anywhere on earth). https://softconf.com/emnlp2023/ArgMining2023/ For the ARR commitment process, we will provide details in our second call for paper later in the summer. Double Blind Review ArgMining 2023 will follow the ACL policies for preserving the integrity of double-blind review for long and short paper submissions. Papers must not include authors’ names and affiliations. Furthermore, self-references or links (such as github) that reveal the author’s identity, e.g., “We previously showed (Smith, 1991) …” must be avoided. Instead, use citations such as “Smith previously showed (Smith, 1991) …” Papers that do not conform to these requirements will be rejected without review. Papers should not refer, for further detail, to documents that are not available to the reviewers. For example, do not omit or redact important citation information to preserve anonymity. Instead, use third person or named reference to this work, as described above (“Smith showed” rather than “we showed”). If important citations are not available to reviewers (e.g., awaiting publication), these paper/s should be anonymised and included in the appendix. They can then be referenced from the submission without compromising anonymity. Papers may be accompanied by a resource (software and/or data) described in the paper, but these resources should also be anonymized. Unlike long and short papers, demo descriptions will not be anonymous. Demo descriptions should include the authors’ names and affiliations, and self-references are allowed. ANONYMITY PERIOD (taken from the EMNLP call for papers in verbatim for the most part) The following rules and guidelines are meant to protect the integrity of double-blind review and ensure that submissions are reviewed fairly. The rules make reference to the anonymity period, which runs from 1 month before the direct submission deadline (starting August 1, 2023) up to the date when your paper is accepted or rejected (October 7, 2023). For papers committed from ARR, the anonymity period starts August 15, 2023. Papers that are withdrawn during this period will no longer be subject to these rules. You may not make a non-anonymized version of your paper available online to the general community (for example, via a preprint server) during the anonymity period. Versions of the paper include papers having essentially the same scientific content but possibly differing in minor details (including title and structure) and/or in length. If you have posted a non-anonymized version of your paper online before the start of the anonymity period, you may submit an anonymized version to the conference. The submitted version must not refer to the non-anonymized version, and you must inform the programme chairs that a non-anonymized version exists. You may not update the non-anonymized version during the anonymity period, and we ask you not to advertise it on social media or take other actions that would further compromise double-blind reviewing during the anonymity period. You may make an anonymized version of your paper available (for example, on OpenReview), even during the anonymity period. For arXiv submissions, August 1, 2023 11:59pm UTC-12h (anywhere on earth) is the latest time the paper can be uploaded if you plan a direct submission to the workshop (or August 15, 2023 for papers from ARR committed to the workshops on September 15, 2023). BEST PAPER AWARDS In order to recognize significant advancements in argument mining science and technology, ArgMining 2023 will include best paper awards. All papers at the workshop are eligible for the best paper awards and a selection committee consisting of prominent researchers in the fields of interest will select the recipients of the awards. SHARED TASKS (Submission closed!) We will be hosting two shared tasks this year: - ImageArg-Shared-Task-2023: The First Shared Task in Multimodal Argument Mining - PragTag-2023: The First Shared Task on Pragmatic Tagging of Peer Reviews ArgMining 2023 ORGANIZING COMMITTEE Milad Alshomary, Leibniz University Hannover, Germany Chung-Chi Chen, National Institute of Advanced Industrial Science and Technology, Japan Smaranda Muresan, Columbia University, USA Joonsuk Park, University of Richmond, USA Julia Romberg, Heinrich Heine University of Duesseldorf, Germany

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FinCausal 2023: Training set released (English & Spanish)
by FinCausal 2023 11 Aug '23

11 Aug '23

*FinCausal 2023: Financial Document Causality Detection* We are glad to announce that the Training Dataset for both English and Spanish is released and ready on Codalab in this link: https://codalab.lisn.upsaclay.fr/competitions/14596 Please register on CodaLab and get to the FInCausal.2023 Competition. Under Participate, you will find the Training Datasets together with a Starting Kit to guide you through the Task. ###### *Task Description and Important Links *####### *FinCausal-2023 Shared Task: “Financial Document Causality Detection” *is organised within the *5th Financial Narrative Processing Workshop (FNP 2023)* taking place in the 2023 IEEE International Conference on Big Data (IEEE BigData 2023) <http://bigdataieee.org/BigData2023/>, Sorrento, Italy, 15-18 December 2023. It is a *one-day event*. Workshop URL: https://wp.lancs.ac.uk#####cfie/fincausal2023/ <https://wp.lancs.ac.uk/cfie/fincausal2023/> ###### *Additional Information *####### *Shared Task Description:* Financial analysis needs factual data and an explanation of the variability of these data. Data state facts but need more knowledge regarding how these facts materialised. Furthermore, understanding causality is crucial in studying decision-making processes. The *Financial Document Causality Detection Task* (FinCausal) aims at identifying elements of cause and effect in causal sentences extracted from financial documents. Its goal is to evaluate which events or chain of events can cause a financial object to be modified or an event to occur, regarding a given context. In the financial landscape, identifying cause and effect from external documents and sources is crucial to explain why a transformation occurs. Two subtasks are organised this year. *English FinCausal subtask *and* Spanish FinCausal subtask*. This is the first year where we introduce a subtask in Spanish. *Objective*: For both tasks, participants are asked to identify, given a causal sentence, which elements of the sentence relate to the cause, and which relate to the effect. Participants can use any method they see fit (regex, corpus linguistics, entity relationship models, deep learning methods) to identify the causes and effects. *English FinCausal subtask* - *Data Description: *The dataset has been sourced from various 2019 financial news articles provided by Qwam, along with additional SEC data from the Edgar Database. Additionally, we have augmented the dataset from FinCausal 2022, adding 500 new segments. Participants will be provided with a sample of text blocks extracted from financial news and already labelled. - *Scope: *The* English FinCausal subtask* focuses on detecting causes and effects when the effects are quantified. The aim is to identify, in a causal sentence or text block, the causal elements and the consequential ones. Only one causal element and one effect are expected in each segment. - *Length of Data fragments: *The* English FinCausal subtask* segments are made up of up to three sentences. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format. This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context regarding a previous situation. So, the task will emphasise the detection of causality associated with the transformation of financial objects embedded in quantified facts. *Spanish FinCausal subtask* - *Data Description: *The dataset has been sourced from a corpus of Spanish financial annual reports from 2014 to 2018. Participants will be provided with a sample of text blocks extracted from financial news, labelled through inter-annotator agreement. - *Scope: *The *Spanish FinCausal subtask* aims to detect all types of causes and effects, not necessarily limited to quantified effects. The aim is to identify, in a paragraph, the causal elements and the consequential ones. Only one causal element and one effect are expected in each paragraph. - *Length of Data fragments: *The *Spanish FinCausal subtask* involves complete paragraphs. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format. This shared task focuses on determining causality associated with both events or quantified facts. For this task, a cause can be the justification for a statement or the reason that explains a result. This task is also a relation detection task. Best regards, FinCausal 2023 Team

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[CfP] DBpedia Day @ SEMANTiCS on Sep 20
by DBpedia 11 Aug '23

11 Aug '23

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Postdoc position in Cambridge (deadline 15/9)
by A. Vlachos 10 Aug '23

10 Aug '23

Dear all, We are looking for a Research Assistant/Associate to work on the project Automated Verification of Textual Claims (AVeriTeC), an ERC project led by Prof. Andreas Vlachos at the University of Cambridge starting in January 2024 or as soon as possible thereafter. The position is for two years. The successful candidate will be based in the Natural Language and Information Processing group (http://www.cl.cam.ac.uk/research/nl/) at the Department of Computer Science and Technology. The project focuses on developing approaches enabling the verification of highly complex claims, which require multiple pieces of evidence. Special focus will be paid to accompanying the verdicts with suitable justifications. Candidates will have completed a Ph.D. (or be close to completing it) in a relevant field such as NLP, Information Retrieval, Artificial Intelligence or Machine Learning and be able to demonstrate a strong track record of independent research and high-quality publications. Essential skills include excellent programming (Python), NLP techniques, Machine Learning, and proven communication skills. Appointment at research associate level is dependent on having a PhD or having equivalent skills and experience through non-academic routes. Where a PhD has yet to be awarded appointment will initially be made as a research assistant and amended to research associate when the PhD is awarded. Enquiries concerning this position should be directed to Prof. Andreas Vlachos (av308(a)cam.ac.uk), and applicants are encouraged to contact him regarding the position. Apply using this link: https://www.jobs.cam.ac.uk/job/42366/ Thanks, Andreas

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Free event: 22 September - Business and Professional Communication
by Brezina, Vaclav 10 Aug '23

10 Aug '23

Dear all, We are organising a free hybrid event (online and in person): Language Data Analysis for Business and Professional Communication. It will take place on 22 September 2023 10:00 - 15:30 UK time. More details and registration: https://cass.lancs.ac.uk/mycalendar-events/?event_id1=3470 The ESRC Centre for Corpus Approaches to Social Science, Lancaster University offers a practical training workshop focused on computational analysis of language data for businesses and professional organisations and anyone interested in communication in professional contexts. The data includes social media, newspapers, business reports, marketing materials and other data sources. The workshop will introduce a new software tool #LancsBox X<https://lancsbox.lancs.ac.uk/> developed at Lancaster University, which can analyse and visualise large amounts of language data (millions and billions of words). Practical examples of uses of #LancsBox X (case studies) will be provided. Best, Vaclav Professor Vaclav Brezina Professor in Corpus Linguistics Department of Linguistics and English Language ESRC Centre for Corpus Approaches to Social Science Faculty of Arts and Social Sciences, Lancaster University Lancaster, LA1 4YD Office: County South, room C05 T: +44 (0)1524 510828 [cid:02038f34-9006-4e55-866f-0bc6e77964ba]@vaclavbrezina [cid:30848364-59cf-4561-a71f-9c3e4ac4519c]<http://www.lancaster.ac.uk/arts-and-social-sciences/about-us/people/vaclav-…>

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[FinNLP@IJCNLP-AACL-2023] Call for Paper & ML-ESG-2 Shared Task
by CHUNG-CHI CHEN 10 Aug '23

10 Aug '23

Dear Colleagues, I'm delighted to share that The 6th Workshop on Financial Technology and Natural Language Processing (FinNLP) will be collocated with IJCNLP-AACL-2023 (http://www.ijcnlp-aacl2023.org/) in Bali, Indonesia (November 1–4). The submission due for the main track paper is *Sep. 8th, 2023*. For more details, please refer to the website: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp2023/home We continue the *ML-ESG shared task* in FinNLP, and share new labels related to Impact Type Identification. You can also get more details about the shared task on the FinNLP website. The Registration Form is ready: https://forms.gle/j6gL5jy1upq5LrKY9 Participants in previous FinNLP share various insights into NLP in FinTech applications. Please refer to past years' proceedings for more details: https://aclanthology.org/venues/finnlp/ Feel free to let us know if you have any questions. Looking forward to seeing you at the 6th FinNLP. Best Regards, Chung-Chi --- 陳重吉 (Chung-Chi Chen), Ph.D. Researcher Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Japan E-mail: c.c.chen(a)acm.org <cjchen(a)nlg.csie.ntu.edu.tw> Website: http://cjchen.nlpfin.com

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