- Corpora - ELRA lists

Re: др
by Slava Mikhaylov 10 Jun '23

10 Jun '23

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CFP: Presentation at CIKM 2023 Industry Day
by jiyinhe＠gmail.com 10 Jun '23

10 Jun '23

The Industry Day of CIKM ’23 will be held on Sunday 22nd Oct 2023 in Birmingham, UK. As with the main conference, which will be held on-site, we anticipate that all presentations for the Industry Day will be delivered in person. Exceptions may be made in case of severe travelling restrictions. We call for technical talks which will cover how topics of interest relevant to the broader CIKM community, including but not limited to knowledge management, information retrieval, efficient data processing, neural and large language models, evaluation, recommender systems, data mining, and others found in the CIKM ‘23 Call for Papers are used in an industrial setting. For example, how machine learning is put to use in practical scenarios, how user behaviour can be observed and interpreted, how to improve systems in practice, how industrial pipelines can be optimised, and how scale is a challenge in more ways than the obvious. We also encourage talk proposals from small companies, such as startups or spin-offs from either a university project or a large company Talks may address challenges, solutions, and case studies of interesting and innovative systems in areas including but not limited to: * Innovative approaches used in deployed systems and product * System design from industry practitioners which identify best practices and design principles for machine learning systems and their scalability aspects * Metrics and measurement techniques used to understand performance of production systems * Practical challenges such as data, privacy, integrity, scale, regulation, etc. * Domain specific challenges and niche focuses * Connections with academia to solve interesting problems, including talk proposals from academics spending time in industry, or vice-versa, covering insights for other practitioners The authors of accepted proposals will be invited to submit an abstract to be published in the conference proceeding. Each presentation will be 15-20 minutes long including Q&A. Submissions should include: Title and abstract Speaker's bio Relevance to above themes and CIKM topics CIKM is a technical conference, so preference will be given to talks describing applied research and technical challenges rather than product presentations. Speakers will be asked to confirm their presence at the conference if their submission is accepted. Submission Instructions Proposals should be at most 2 pages and follow the ACM format. Formatting guidelines are available at the ACM Website (use the ˮsigconf” proceedings template). https://www.acm.org/publications/proceedings-template Submissions are not anonymous and should contain speaker details. Proposals should be submitted electronically via EasyChair: https://easychair.org/my/conference?conf=cikm23 Important Dates: - All deadlines are at 11:59pm in the Anywhere on Earth time zone. - Submissions Due: July 14, 2023 - Notifications: August 11, 2023 - Camera ready for abstracts (no exceptions): August 18, 2023 Industry Day Chairs Jiyin He, Signal AI, UK Jeremy Pickens, Redgrave Data, USA Contact: cikm2023-industry(a)easychair.org

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Re: Visualizing graphs of words as nodes from text lines (not just adjacency pairs) ...
by Albretch Mueller 10 Jun '23

10 Jun '23

On 6/9/23, Serge Heiden <slh(a)ens-lyon.fr> wrote: > Hi Albretch, > > For some ideas, I made something related to that more than 20 years ago: > https://shs.hal.science/halshs-00151838v1/document (in French sorry, > just looking at the graphs at the end should be informative) > Not as interactive as it could, but UI technologies have evolved now. > > Best, > Serge > Nothing to apologize for, last time I checked (speaking or) writing a paper in French or any other language other than Russian wasn't illegal or wrong in any way (USG has made "radioactive" even the use of any English word starting with "Vlad" or "Put"). Also, one of my Ls is Spanish, I am fairly acquainted with Latin and know or can easily infer a good chuck of French words. translate.google.com does a relatively fine job of blunt translation of the general sense of texts. For some reason I had to download your paper and upload to google translate to be able to read it in English, since it has even taken over as my preferred curse language. I found your paper very interesting, so I gave it a first reading to think about it and reread it more carefully later when I found the time to do so. Furthermore (how do you say „darüberhinausgehend" in English? ;-)), those "face-à-faces" kinds of political debates, seen as forceful "conversations", -conversations nonetheless-, bring about a whole host of corpora research issues I am sure you/those studying such topics must have noticed. There are different kinds of "conversations". Friar Leo (Saint Francis' fioretti) and Evodius (Saint Augustine's "De libero arbitrio voluntatis") were both real persons but the conversation of sort was very one-sided with Friar Leo pretty much lending an ear to Saint Francis and Evodius who was more of an equal and Saint Augustine's personal friend. Plato, involved his teacher Socrates, Alcibiades (even a slave in his Meno which in those times was quite an affront to mock the establishment), but also the Egyptian god of writing (Thoth) in his dialogues. Shakespeare's and Dostoevsky's fictional characters have taken a voice and life of their own for many generations. However all those conversations have some aspects in common. Conversations/dialogues are peculiar in the sense that (kind of like with music) you have more than one text realizing more or less hopefully a certain train of thought (Gedankengang), yet all participating texts reflect explicitly/lexically or implicitly in more of a connotative way on one another into some sort of more orchestral narrative, which (as the poet in me sees it) can NOT be fully reduced in a syntactically operational way to just the participating texts. Think of how music is played (when we had such thing) each instrument (in accord with some shared harmonic and rhythmic) doing its own part, but also as part of a Hegelian whole. > “Words have no meaning; they only have uses”. Wittgenstein's quip could lead to meaning, if we knew for a term all of its uses. But this set does not exist. Actually, technically speaking and demonstrably, this is not quite the case. It hasn't been for at least two decades. A back of the envelope calculation will show that, as part of the societally-wide "monitoring" in the breath by breath surveilled societies we live in these days, recording, tracking ("every piece of tangible information" (tm) to "its source") and indexing in a cross-correlated way everything everyone says for good real time is not only feasible, but it is exactly what the NSAs of the world have been very cheaply doing with the happily willing cooperation of proles who (to George Orwell's dismay) can't take their head off from their cell phones' ass. > Because the words depend on the "situations" of use, causes and conditions of their enunciation, and we know well that these vary ad infinitum, ... I have no way of knowing if "ad infinitum" is meant in a metaphoric way. In actual texts, seeing words as nodes in DAGs, "ad infinitum" would mean what?, six (6) words? > ... in time as well as in space. The only solution consists in seizing one of these "situations" where the text is stated, precise, clearly circumscribed, dated, controllable in its essential aspects, then to make, about it, the sum of the words to be studied ... and this is what makes corpora research interesting. "These situations" don't exist ;-). The essentially locked inner- and outer-intersubjectivity of language makes impossible (and/or hopelessly senseless) to clearly circumscribe in controllable ways the essential aspects of what, when, where, how ... a text is stated, because texts don't have a life of their own, nor do they determine or are derived from "factual reality" as if they were just an object, like a piece of stone, in the physically empirical ways we have been conditioned to "rationally" think since the scientific revolution. In a Hegelian („im ,Allgemeinen' Sinne"), every one who listened to, thought about such debates and/or relates to them even in more or less indirect, marginal ways (as we do right now while we talk about it) would be part of "those situations". How could you "clearly circumscribe ..." that? Niggah isn't really into protagonism, but I could relate a (in an early sense the most) formative experience I have had with texts (for whatever reason I have always loved to read alternating more than one book during the same period of time since I was little). I was born into and raised in the Cuba of the 60's (during the most determining, "crazy" periods of "the revolution") as part of a family of high profile political dissidents (imprisoned before and after for political reasons), which taxed, busied the hell out of your mind. From teachers not speaking, not even looking at me during classes (which other kids, of course, noticed and protested/asked about even to their own parents (I myself could not understand it either)), "because" as my mother explained to me, "they had to then write down all I said during classes for the police to keep as part of my profile" (in those times they didn't have cameras, sensors everywhere; people didn't have cell phones); to my mother forcing her children to go to church "because she didn't like that idea of politicians telling people what/how to think" (I could not understand what "politicians" meant, nor could I why they would mess with my wanting to play with my friends)) I could not make sense of most of the hellish weirdness happening around me but I kept thinking and asking my mother about it who would then start telling me about "the banality of evil", ... which I couldn't quite understand either, forcibly turning me into some sort child philosopher. One day I found in a box on its way to the garbage a religious book ("Cien lecciones de historias sagradas") which included the story in "Saint Francis' fioretti" about "what 'perfect' joy consists in". The moral of that story conceptually paraphrased by yours truly as a philosophical statement with a consciousness studies slant to it would be: "the greatest of all gifts and graces that God grants us with is the capacity of overcoming oneself". That one liner overwhelmingly fascinated me (it has to this very day!, its truthfulness, its poetic import, its liberating hopefulness, …!). Later I understood Saint Francis was talking about that thing they used to call "virtue" (which, since you can’t can it and sell it it doesn’t matter much these days), ... and that the underlying aspect his true statement was based on had no explanation (kind of "pulling oneself up by one's own bootstraps" not even using some sort of Archimedean lever, but one's own spirituality. How on earth is that even possible!?!) and how could something Saint Francis said to friar Leo during their wintry talk to Saint Mary's of the Angels in Perugia/Umbria/Central Italy in the XIV century as part of the medieval Zeitgeist, after going through such a long sequence of communicative realizations (from Fr. Ugolino authoring, discussions, reprints, moving the text to another continent, ...) impress so mightily a little boy in Havana some 500+ years later? > From this sum, this exploration and this exhaustive comparison of contexts, the statistical research of co-occurrences can draw an objective description. Does it put us on a track that leads to meaning? Yes, to some extent, but we shouldn't be illusive about that kind of "objective description". The interesting aspect would be: to which extent would claims about "objectivity" demonstrably be, in some sense factual and to which extent we would be, "quite naturally", reading whatever we want into those texts in order to "logically" justify whatever we want? We should keep it real because our work is part of the "AI"-based "social control" (as freedom lovers call -repression-) thoroughly employed these days in all levels of society and against individuals they target. Yet, there must be something to it, because we live and learn through our conscious communicative exchanges. Society at large is kind of a corpus, a hyper forest of decentralized texts (not only "texts" in the way we see them as sequences of characters consciously written on some permanent media from stone, to reed, to paper, to magnetic excitation on a hard drive, but our actions are functional "texts" through which we constantly interacting with one another ...) You buy some veggies paying with some money which you earned by functionally doing whatever with a social import, which doesn't mean the same to you as it does to the farmer growing it, the people who brought it to the store or selling it to you, ..., but it somehow all happens just fine. "Money", "texts", ... semiologically serving as multipurpose accommodating and balancing medium both outer- and inner-intersubjectively, indeed our mind-body link! lbrtchx

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CFP for NLP-OSS @ EMNLP2023
by Elijah Rippeth 09 Jun '23

09 Jun '23

(apologies for cross-posting) ---------------------------------------------------------------- *Workshop for NLP Open Source Software (NLP-OSS)* 06 Dec 2023, Co-located with EMNLP 2023 https://nlposs.github.io/ Deadline for Long and Short Paper submission: 09 August, 2023 (23:59, GMT-11) ---------------------------------------------------------------- You have tried to use the latest, bestest, fastest LLM models and bore grievances but found the solution after hours of coffee and computer staring. Share that at NLP-OSS and suggest how open source could change for the better (e.g. best practices, documentation, API design etc.) You came across an awesome SOTA system on NLP task X and no LLM has beaten its F1 score. However, the code is now stale and it takes a dinosaur to understand the code. Share your experience at NLP-OSS and propose how to "replicate" these forgotten systems. You see this shiny GPT from a blog post, tried it to reproduce similar results on a different task and it just doesn't work on your dataset. You did some magic to the code and now it works. Show us how you did it! Though they're small tweaks, well-motivated and empirically tested are valid submissions to NLP-OSS. You have tried 101 NLP tools and there's none that really do what you want. So you wrote your own shiny new package and made it open source. Tell us why your package is better than the existing tools. How did you design the code? Is it going to be a one-time thing? Or would you like to see thousands of people using it? You have heard enough of open-source LLM and pseudo-open-source GPT but not enough about how it can be used for your use-case or your commercial product at scale. So you contacted your legal department and they explained to you about how data, model and code licenses work. Sharing the knowledge with the NLP-OSS community. You have a position/opinion to share about free vs open vs closed source LLMs and have valid arguments, references or survey/data to support your position. We would want to hear more about it. At last, you've found the avenue to air these issues in an academic platform at the NLP-OSS workshop!!! Sharing your experiences, suggestions and analysis from/of NLP-OSS P/S: 1st CALL FOR PAPERS ==== ---------------------------------------------------------------- *Workshop for NLP Open Source Software (NLP-OSS)* 06 Dec 2023, Co-located with EMNLP 2023 https://nlposs.github.io/ Deadline for Long and Short Paper submission: 09 August, 2023 (23:59, GMT-11) ---------------------------------------------------------------- The Third Workshop for NLP Open Source Software (NLP-OSS) will be co-located with EMNLP 2023 on 06 Dec 2023. Focusing more on the social and engineering aspect of NLP software and less on scientific novelty or state-of-art models, the Workshop for NLP-OSS is an academic forum to advance open source developments for NLP research, teaching and application. NLP-OSS also provides an academic workshop to announce new software/features, promote the collaborative culture and best practices that go beyond the conferences. We invite full papers (8 pages) or short papers (4 pages) on topics related to NLP-OSS broadly categorized into (i) software development, (ii) scientific contribution and (iii) NLP-OSS case studies. - **Software Development** - Designing and developing NLP-OSS - Licensing issues in NLP-OSS - Backwards compatibility and stale code in NLP-OSS - Growing, maintaining and motivating an NLP-OSS community - Best practices for NLP-OSS documentation and testing - Contribution to NLP-OSS without coding - Incentivizing OSS contributions in NLP - Commercialization and Intellectual Property of NLP-OSS - Defining and managing NLP-OSS project scope - Issues in API design for NLP - NLP-OSS software interoperability - Analysis of the NLP-OSS community - **Scientific Contribution** - Surveying OSS for specific NLP task(s) - Demonstration, introductions and/or tutorial of NLP-OSS - Small but useful NLP-OSS - NLP components in ML OSS - Citations and references for NLP-OSS - OSS and experiment replicability - Gaps between existing NLP-OSS - Task-generic vs task-specific software - **Case studies** - Case studies of how a specific bug is fixed or feature is added - Writing wrappers for other NLP-OSS - Writing open-source APIs for open data - Teaching NLP with OSS - NLP-OSS in the industry Submission should be formatted according to the [EMNLP 2023 templates](https://2023.emnlp.org/call-for-papers) and submitted to [OpenReview](https://openreview.net/group?id=EMNLP/2023/Workshop/NLP-OSS) ORGANIZERS Geeticka Chauhan, Massachusetts Institute of Technology Dmitrijs Milajevs, Grayscale AI Elijah Rippeth, University of Maryland Jeremy Gwinnup, Air Force Research Laboratory Liling Tan, Amazon

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Call for participation - Arabic NER Shared Task 2023
by nagham ghanim 09 Jun '23

09 Jun '23

Dear colleagues, We are happy to invite you to join the *Arabic NER SharedTask 2023* <https://dlnlp.ai/st/wojood/> which will be organized as part of the WANLP 2023. We will provide you with a large corpus and Google Colab notebooks to help you reproduce the baseline results. دعوة للمشاركة في مسابقة استخراج الكيونات المسماه من النصوص العربية. سنزود المشاركين بمدونة وبرمجيات للحصول على نتائج مرجعية يمكنهم البناء عليها. *INTRODUCTION* Named Entity Recognition (NER) is integral to many NLP applications. It is the task of identifying named entity mentions in unstructured text and classifying them to predefined classes such as person, organization, location, or date. Due to the scarcity of Arabic resources, most of the research on Arabic NER focuses on flat entities and addresses a limited number of entity types (person, organization, and location). The goal of this shared task is to alleviate this bottleneck by providing Wojood, a large and rich Arabic NER corpus. Wojood consists of about 550K tokens (MSA and dialect, in multiple domains) that are manually annotated with 21 entity types. *REGISTRATION* Participants need to register via this form ( *https://forms.gle/UCCrVNZ2LaPviCZS6* <https://forms.gle/UCCrVNZ2LaPviCZS6>). Participating teams will be provided with common training development datasets. No external manually labelled datasets are allowed. Blind test data set will be used to evaluate the output of the participating teams. Each team is allowed a maximum of 3 submissions. All teams are required to report on the development and test sets (after results are announced) in their write-ups. *FAQ* For any questions related to this task, please check our *Frequently Asked Questions* <https://docs.google.com/document/d/1XE2n89mFLic2P9DO_sAD51vy734BOt0kgtZ6bFf…> *IMPORTANT DATES* - March 03, 2023: Registration available - May 25, 2023: Data-sharing and evaluation on development set Avaliable - June 10, 2023: Registration deadline - July 20, 2023: Test set made available - July 30, 2023: Evaluation on test set (TEST) deadline - Augest 29, 2023: Shared task system paper submissions due - October 12, 2023: Notification of acceptance - October 30, 2023: Camera-ready version - TBA: WANLP 2023 Conference. ** All deadlines are 11:59 PM UTC-12:00 (Anywhere On Earth).* *CONTACT* For any questions related to this task, please contact the organizers directly using the following email address: *NERShare...(a)gmail.com <https://groups.google.com/>* or join the google group: *https://groups.google.com/g/ner_sharedtask2023* <https://groups.google.com/g/ner_sharedtask2023>. *SHARED TASK* As described, this shared task targets both flat and nested Arabic NER. The subtasks are: *Subtask 1:* *Flat NER* In this subtask, we provide the Wojood-Flat train (70%) and development (10%) datasets. The final evaluation will be on the test set (20%). The flat NER dataset is the same as the nested NER dataset in terms of train/test/dev split and each split contains the same content. The only difference in the flat NER is each token is assigned one tag, which is the first high-level tag assigned to each token in the nested NER dataset. *Subtask 2:* *Nestd NER* In this subtask, we provide the Wojood-Nested train (70%) and development (10%) datasets. The final evaluation will be on the test set (20%). *METRICS* The evaluation metrics will include precision, recall, F1-score. However, our official metric will be the micro F1-score. The evaluation of shared tasks will be hosted through CODALAB. Teams will be provided with a CODALAB link for each shared task. -*CODALAB link for NER Shared Task Subtask 1 (Flat NER)* <https://codalab.lisn.upsaclay.fr/competitions/11594> -*CODALAB link for NER Shared Task Subtask 2 (Nestd NER)* <https://dlnlp.ai/st/wojood/> *BASELINES* Two baseline models trained on Wojood (flat and nested) are provided: *Nested NER baseline:* is presented in this *article* <https://aclanthology.org/2022.lrec-1.387/>, and code is available in *GitHub* <https://github.com/SinaLab/ArabicNER>. The model achieves a micro F1-score of 0.9059 (note that this baseline does not handle nested entities of the same type). *Flat NER baseline:* same code repository for nested NER (*GitHub* <https://github.com/SinaLab/ArabicNER>) can also be used to train flat NER task. Our flat NER baseline achieved a micro F1-score of 0.8785. *GOOGLE COLAB NOTEBOOKS* To allow you to experiment with the baseline, we authored four Google Colab notebooks that demonstrate how to train and evaluate our baseline models. [1] *Train Flat NER* <https://gist.github.com/mohammedkhalilia/72c3261734d7715094089bdf4de74b4a>: This notebook can be used to train our ArabicNER model on the flat NER task using the sample Wojood data found in our repository. [2] *Evaluate Flat NER* <https://gist.github.com/mohammedkhalilia/c807eb1ccb15416b187c32a362001665>: this notebook will use the trained model saved from the notebook above to perform evaluation on unseen dataset. [3] *Train Nested NER* <https://gist.github.com/mohammedkhalilia/a4d83d4e43682d1efcdf299d41beb3da>: This notebook can be used to train our ArabicNER model on the nested NER task using the sample Wojood data found in our repository. [4] *Evaluate Nested NER* <https://gist.github.com/mohammedkhalilia/9134510aa2684464f57de7934c97138b>: this notebook will use the trained model saved from the notebook above to perform evaluation on unseen dataset. *ORGANIZERS* - Mustafa Jarrar, Birzeit University - Muhammad Abdul-Mageed, University of British Columbia & MBZUAI - Mohammed Khalilia, Birzeit University - Bashar Talafha, University of British Columbia - AbdelRahim Elmadany, University of British Columbia - Nagham Hamad, Birzeit University - Alaa Omer, Birzeit University

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Journal of Open Humanities Data: 2023 call for papers (second call, with a twist)
by Barbara McGillivray 09 Jun '23

09 Jun '23

Dear all, with apologies for cross-postings, I'm sharing again the 2023 call for papers of the Journal of Open Humanities Data. This time we've added an explicit mention of large language model prompts and prompt engineering strategies among the language resources of interest to the journal, plus a reminder that our Covid-19 special collection is still accepting submissions. We've also explicitly included Library Science and Media Studies in the scope. Kind regards, Barbara Call for Papers for 2023 The Journal of Open Humanities Data (JOHD)<https://openhumanitiesdata.metajnl.com/> features peer-reviewed publications describing humanities research objects with high potential for reuse. These might include curated resources like (annotated) linguistic corpora, ontologies, and lexicons, as well as databases, maps, atlases, linked data objects, and other data sets created with qualitative, quantitative, or computational methods, including large language model prompts and prompt engineering strategies. We are currently inviting submissions of two varieties: 1. Short data papers contain a concise description of a humanities research object with high reuse potential. These are short (1,000 words) highly structured narratives. A data paper does not replace a traditional research article, but rather complements it. 2. Full length research papers discuss and illustrate methods, challenges, and limitations in humanities research data creation, collection, management, access, processing, or analysis. These are intended to be longer narratives (3,000 - 5,000 words), which give authors the ability to contribute to a broader discussion regarding the creation of research objects or methods. Humanities subjects of interest to the JOHD include, but are not limited to Art History, Classics, History, Library Science, Linguistics, Literature, Media Studies, Modern Languages, Music and musicology, Philosophy, Religious Studies, etc. Research that crosses one or more of these traditional disciplinary boundaries is highly encouraged. Authors are encouraged to publish their data in recommended repositories<https://openhumanitiesdata.metajnl.com/about/#repo>. More information about the submission process<https://openhumanitiesdata.metajnl.com/about/submissions>, editorial policies<https://openhumanitiesdata.metajnl.com/about/editorialpolicies/> and archiving<https://openhumanitiesdata.metajnl.com/about/> is available on the journal’s web pages. Submissions are still open for our special collection, Humanities Data in the Time of COVID-19<https://openhumanitiesdata.metajnl.com/collections/humanities-data-in-the-t…>. This collection includes data papers that span various areas of enquiry about the COVID-19 pandemic through the lens of the Humanities. Data from this period have far-reaching and impactful reuse potential, so we encourage you to share your data by submitting to this growing collection. JOHD provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. We accept online submissions via our journal website. See Author Guidelines <https://openhumanitiesdata.metajnl.com/about/submissions/> for further information. Alternatively, please contact the editor<https://openhumanitiesdata.metajnl.com/contact/> if you are unsure as to whether your research is suitable for submission to the journal. Authors remain the copyright holders and grant third parties the right to use, reproduce, and share the article according to the Creative Commons<http://creativecommons.org/licenses/by/4.0/> licence agreement.

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Re: Visualizing graphs of words as nodes from text lines (not just adjacency pairs) ...
by Albretch Mueller 09 Jun '23

09 Jun '23

Thank you! igraph seems to be more Linux/Debian friendly. There is a "GNU R network analysis and visualization" package: r-cran-igraph So far I have found: https://cran.r-project.org/web/packages/igraph/ https://cran.r-project.org/web/packages/igraph/igraph.pdf and a bunch of videos/tutorials, which I will have a better opinion about after I watch them. I will keep publicly posting my experiences to help those running against the same kinds of problems. $ time apt-cache search gephi real 0m0.267s user 0m0.255s sys 0m0.012s $ time apt-cache search igraph karbon - vector graphics application for the Calligra Suite cl-graph - simple graph data structure and algorithms libdirgra-java - Java library providing a simple directed graph implementation libdirgra-java-doc - Documentation for dirgra fonts-bajaderka - Warsaw's sign painters styled font fonts-gfs-neohellenic - modern Greek font family with matching Latin fonts-gfs-solomos - ancient Greek oblique font fonts-isabella - Isabella free TrueType font fonts-sil-annapurna - smart font for languages using Devanagari script fonts-uralic - Truetype fonts for Cyrillic-based Uralic languages golang-github-guptarohit-asciigraph-dev - Make lightweight ASCII line graph in CLI apps with no other dependencies golang-github-jesseduffield-asciigraph-dev - Go package to make lightweight ASCII line graph without dependencies golang-github-steveyen-gtreap-dev - gtreap is an immutable treap implementation in the Go Language gpw - Trigraph Password Generator libigraph-dev - library for creating and manipulating graphs - development files libigraph-examples - library for creating and manipulating graphs - example files libigraph1 - library for creating and manipulating graphs libjgrapht0.6-java - mathematical graph theory library for Java libjgrapht0.8-java - mathematical graph theory library for Java libtext-password-pronounceable-perl - Perl module to generate pronounceable passwords liwc - Tools for manipulating C source code msort - utility for sorting records in complex ways libnauty2 - library for graph automorphisms -- library package libnauty2-dev - library for graph automorphisms -- development package nauty - library for graph automorphisms -- interface and tools nauty-doc - library for graph automorphisms -- user guide otp - Generator for One Time Pads or Passwords perl-tk - Perl module providing the Tk graphics library python3-igraph - High performance graph data structures and algorithms (Python 3) r-cran-graphlayouts - GNU R additional layout algorithms for network visualizations r-cran-gwidgets - gWidgets API for Toolkit-Independent, Interactive GUIs r-cran-igraph - GNU R network analysis and visualization r-cran-propclust - Propensity Clustering and Decomposition scalable-cyrfonts-tex - Scalable Cyrillic fonts for TeX texlive-pictures - TeX Live: Graphics, pictures, diagrams texlive-fonts-extra - TeX Live: Additional fonts texlive-latex-extra - TeX Live: LaTeX additional packages tran - transcribe between character scripts (alphabets) vis - Modern, legacy free, simple yet efficient vim-like editor real 0m0.303s user 0m0.283s sys 0m0.020s $ On 6/9/23, David Chartash <dchartas(a)ieee.org> wrote: > Hi Albretch, > I would start off with Gephi <https://gephi.org/> or try the R/C/Python... > package igraph <https://igraph.org/>. > Cheers, > > David > --- > Please forgive any spelling errors, sent from a poorly implemented software > keyer > > On Fri, Jun 9, 2023, 02:40 Albretch Mueller via Corpora < > corpora(a)list.elra.info> wrote: > >> I could imagine, as John Lennon used to sing, that "I am not the only >> one" in need of such an application. >> >> At times you get ten of thousand lines which you would like to >> quickly “visually parse” to gain a general sense of what you've got. >> Ideally, you should be able to play with it to select the records you >> need. >> >> Think for example, of the many links to texts you would get from >> archive.org (which also includes some metadata) or *.pub (each site >> using their own quirkiness) >> >> Based on some sort of GUI, you would see weighted terms (coloured or >> not based on a user's preference) with all other terms preceding (as >> some sort of tree-like structure confluent on that term) and following >> it ( ... branching off of it). >> >> Which kind of applications people use to do such thing? >> >> lbrtchx >> _______________________________________________ >> Corpora mailing list -- corpora(a)list.elra.info >> https://list.elra.info/mailman3/postorius/lists/corpora.list.elra.info/ >> To unsubscribe send an email to corpora-leave(a)list.elra.info >> >

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Visualizing graphs of words as nodes from text lines (not just adjacency pairs) ...
by Albretch Mueller 09 Jun '23

09 Jun '23

I could imagine, as John Lennon used to sing, that "I am not the only one" in need of such an application. At times you get ten of thousand lines which you would like to quickly “visually parse” to gain a general sense of what you've got. Ideally, you should be able to play with it to select the records you need. Think for example, of the many links to texts you would get from archive.org (which also includes some metadata) or *.pub (each site using their own quirkiness) Based on some sort of GUI, you would see weighted terms (coloured or not based on a user's preference) with all other terms preceding (as some sort of tree-like structure confluent on that term) and following it ( ... branching off of it). Which kind of applications people use to do such thing? lbrtchx

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FInCausal 2023 Shared Task: Call for Participation (English & Spanish)
by FinCausal 2023 08 Jun '23

08 Jun '23

Call for Participation: *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*. The exact date is to be announced. Important Dates: - Call for participation and registration: 3rd June 2023 - Registration deadline: 28 June - Training set release: 29 June 2023 - Test set release: 5 September 2023 - Systems submission deadline: 15 September 2023 - Release of results: 20 September 2023 - Paper submission deadline: 20 October 2023 - Notification of acceptance: November 12, 2023 - Camera-ready of accepted papers: November 20, 2023 - FNP Workshop: December 2023 Workshop URL: https://wp.lancs.ac.uk/cfie/fincausal2023/ Registration Form: https://forms.gle/29E161a8RmMosBLU8. After completing the registration form, the practice set will be sent to participants. *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. *FinCausal Shared Task Organisers:* - Antonio Moreno-Sandoval (UAM, Spain) - Blanca Carbajo Coronado (UAM, Spain) - Doaa Samy (UCM, Spain) - Jordi Porta (UAM, Spain) - Dominique Mariko (Yseop, France) For any questions, please contact the organisers at *fincausal.2023(a)gmail.com <fincausal.2023(a)gmail.com>*

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Call for Participation in Shared Tasks (WANLP2023): The 1st Arabic Natural Language Processing Conference
by Salam Khalifa 08 Jun '23

08 Jun '23

*** Apologies for Cross-Posting *** We are pleased to announce five exciting shared tasks <https://wanlp2023.sigarab.org/shared-tasks> as part of the 1st Conference on Arabic NLP (WANLP2023 <https://wanlp2023.sigarab.org/>) - co-located with EMNLP 2023 <https://2023.emnlp.org/> in Singapore. Please refer to the tasks’ websites for more information on participation and internal deadlines. General shared tasks deadlines are at the end of this call. Shared Task 1: NADI 2023 Description: Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. In addition to nuanced dialect identification at the country level, NADI 2022 offered a new subtask focused on country-level sentiment analysis. NADI 2023 continues this tradition of extending tasks beyond dialect identification. Namely, we propose a new open track subtask focused on machine translation (MT) in two directions (i) into Modern Standard Arabic (MSA) from five Arabic dialects and (ii) into any of the five dialects from MSA. Organizers: Muhammad Abdul-Mageed, Chiyu Zhang, El Moatez Billah Nagoudi, AbdelRahim Elmadany (The University of British Columbia, Canada), Houda Bouamor (Carnegie Mellon University, Qatar), and Nizar Habash (New York University Abu Dhabi). For more information, please visit the shared task’s website: <https://sites.google.com/view/arabic-gender-rewriting/> https://nadi.dlnlp.ai/ –––––––––––––––––––––––––––––– Shared Task 2: ArAIEval - Persuasion Techniques and Disinformation Detection in Arabic Text Description: The ArAIEval shared tasks include two tasks: (i) persuasion techniques detection, and (ii) disinformation detection. Organizers: Firoj Alam, Hamdy Mubarak, Maram Hasanain, Wajdi Zaghouani, Giovanni Da San Martino, and Preslav Nakov. For more information, please visit the shared task’s website: <https://sites.google.com/view/arabic-gender-rewriting/> https://araieval.gitlab.io/ –––––––––––––––––––––––––––––– Shared Task 3: Qur'an QA 2023 Description: This is a shared task of Arabic Reading Comprehension over the Holy Qur’an, aiming to trigger state-of-the-art question-answering and reading comprehension research on a book that is sacredly held by more than 1.8 billion people across the world. The shared task entails two subtasks: (A) Passage Retrieval (PR) task, and (B) Reading Comprehension (RC) task. Organizers: Tamer Elsayed, Rana Malhas, Watheq Mansour (Qatar University). For more information, please visit the shared task’s website: <https://sites.google.com/view/arabic-gender-rewriting/> https://sites.google.com/view/quran-qa-2023 –––––––––––––––––––––––––––––– Shared Task 4: WojoodNER Description: Due to the scarcity of Arabic resources, most of the research on Arabic NER focuses on flat entities and addresses a limited number of entity types (person, organization, and location). The goal of this shared task is to alleviate this bottleneck by providing Wojood; a large and rich Arabic NER corpus. Organizers: Muhammad Abdul-Mageed, Mohammed Khalilia, Nagham Hamad, Bashar Talafha, AbdelRahim Elmadany, Alaa’ Omar, Mustafa Jarrar. For more information, please visit the shared task’s website: <https://sites.google.com/view/arabic-gender-rewriting/> https://dlnlp.ai/st/wojood/ –––––––––––––––––––––––––––––– Shared Task 5: Arabic Reverse Dictionary Description: This shared task aims to address the Tip-of-Tongue (TOT) problem by developing a Reverse Dictionary (RD) system specifically for the Arabic language. Reverse dictionaries allow users to find words based on their meanings or definitions and can be useful for writers, crossword puzzle enthusiasts, non-native language learners, and anyone looking to expand their vocabulary. This shared task includes two subtasks: Arabic RD and Cross-lingual Reverse Dictionary (CLRD). Organizers: Rawan Al-Matham, Waad Alshammari, Abdulrahman AlOsaimy, Sarah Alhumoud, Afrah Altamimi, Abdullah Alfaifi. For more information, please visit the shared task’s website: https://samai.ksaa.gov.sa/sharedTask.html –––––––––––––––––––––––––––––– Important dates: - August 29, 2023: shared task papers due date - October 12, 2023: notification of acceptance - October 20, 2023: camera-ready papers due - December 7, 2023: Conference Day All deadlines are 11:59 pm UTC -12h <https://www.timeanddate.com/time/zone/timezone/utc-12> (“Anywhere on Earth”). The WANLP 2023 Publicity Chairs, Salam Khalifa and Amr Keleg -- Salam Khalifa PhD Student at Stony Brook Linguistics <https://www.linguistics.stonybrook.edu/>.

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