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Call for Papers: ACL 2023 Student Research Workshop
by Devamanyu Hazarika 15 Dec '22

15 Dec '22

Student Research Workshop co-located with ACL 2023 in Toronto, Canada (July 10th-12th, 2023) ***Paper Submission Deadline: May 5th, 2023*** Important Dates - Pre-submission mentoring deadline: March 10, 2023 - Pre-submission feedback: April 26, 2023 - Paper submission deadline: May 5, 2023 - Review deadline: May 20, 2023 - Acceptance notifications: May 30 2023 - Camera-ready deadline: June 6, 2023 - ACL 2023 conference dates: July 10-12, 2023 All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth"). About the Student Research Workshop The ACL 2023 Student Research Workshop (SRW) is a forum to bring together students investigating various areas of Computational Linguistics, Natural Language Processing and Machine Learning. The workshop provides an excellent opportunity for participants to present their work and to receive mentorship and valuable feedback from the international research community. The workshop's goal is to aid students at multiple stages of their education, including undergraduate, MSc/MA, junior and senior PhD students, in getting familiar with conducting and presenting their research. General Rules for Submission We invite papers in two different categories: - Research Papers: Papers in this category can describe completed work, or work in progress with preliminary results. For these papers, the first author **MUST BE** a current student (graduate or undergraduate). Topics of interest for the SRW are the same as for the main ACL 2023 conference: <https://www.2022.aclweb.org/calls> https://2023.aclweb.org/calls/main_conference/ - Thesis Proposals: This category is appropriate for PhD students who have decided on a thesis topic and wish to get feedback on their proposal and broader ideas for their continuing work. Submissions (in both categories) may either be archival or non-archival, based on the wish of the authors. All archival papers will be published in the ACL 2023 SRW Proceedings. Non-archival papers may be submitted to any venue in the future except for another SRW. The submission links are on our website (https://acl2023-srw.github.io/). Please read the guidelines carefully. Why Submit to ACL SRW? There are many good reasons to submit to the ACL SRW, such as: - Mentorship program: ACL SRW provides a unique opportunity for students to receive constructive feedback and to improve their work through a pre-submission mentorship program. - Improving your publication record: publishing a paper as an undergraduate or as a MSc/MA student is beneficial when applying for a PhD program. Publishing a paper in an ACL SRW workshop can be really helpful for improving students’ publication record. - Explorative Studies: We encourage the submission of studies with positive and negative results providing insights on why and in which scenarios a particular method succeeds and fails. All accepted papers and thesis proposals will be presented either as oral presentations or during poster sessions, which will give students an opportunity to interact with and to present their work to a large and diverse audience, including top researchers in the field and assigned mentors. Pre-Submission Mentorship Program The SRW offers students the opportunity to receive feedback prior to submitting their work for review. The goal of the pre-submission mentorship program is to improve the quality of writing and presentation of the student's work, not to critique the work itself. Participation is optional but encouraged. The pre-submission mentorship is not anonymous. Students wishing to participate in the pre-submission mentorship must submit their paper draft by March 10, 2023. Note that even though the mentoring is not done anonymously, the paper needs to be anonymized. We will check for the formality of the paper including formatting before we match it with mentors. The participants will be assigned a mentor who will review and will provide feedback within four weeks. This mentor will not be the same person who will review the final submission. The feedback will be in the form of guidelines and suggestions to improve the overall writing, which should ideally be incorporated before the actual submission deadline. You CAN submit a paper at the SRW submission deadline even if you did not participate in the pre-submission mentoring. If you did submit a draft for pre-submission mentoring, you will need to make a new submission for the final version of the paper. The submission website will have separate tracks for pre-submission mentorship and the final paper submission. Submission Requirements We accept both archival submissions (which will be included in the conference proceedings) and non-archival submissions (which will be presented at the workshop but will not be included in the proceedings). All submissions (archival and non-archival) must follow the anonymity period and the restrictions of the main conference. Long papers consist of up to eight (8) pages of content, plus an unlimited number of pages for references and supplementary material like the appendix. Upon acceptance, papers will be given one additional page of content (up to 9 pages). Short papers consist of up to four (4) pages of content, plus an unlimited number of pages for references and supplementary material like the appendix. Upon acceptance, papers will be given one additional page of content (up to 5 pages). Authors are encouraged to use the additional page to address reviewers’ comments. Paper submissions must use the official ACL style templates, which are available as an Overleaf template <https://www.overleaf.com/read/crtcwgxzjskr> and also downloadable directly <https://github.com/acl-org/ACLPUB/tree/master/templates> (Latex and Word). We strongly encourage participants to use the Latex template. All submissions must be in PDF format and must conform to the official style guidelines, which are contained in these template files. The review process will be double-blind, and thus all submissions must be anonymized. The SRW invites papers on topics related to computational linguistics, including but not limited to the following: - Computational Social Science and Social Media - Dialogue and Interactive Systems - Discourse and Pragmatics - Ethics and NLP - Information Extraction - Information Retrieval and Text Mining - Interpretability and Analysis of Models for NLP - Language Grounding to Vision, Robotics, and Beyond - Linguistic Theories, Cognitive Modeling, and Psycholinguistics - Machine Learning for NLP - Machine Translation and Multilinguality - NLP Applications - Phonology, Morphology, and Word Segmentation - Question Answering - Resources and Evaluation - Semantics: Lexical - Semantics: Sentence-level Semantics, Textual Inference, and Other Areas - Sentiment Analysis, Stylistic Analysis, and Argument Mining - Speech and Multimodality - Summarization - Syntax: Tagging, Chunking, and Parsing Grants We expect to have grants to offset some portion of students' travel, conference registration, and accommodation expenses. Further details will be posted on the SRW website. To contact the organizers of the workshop, please email us at: acl2023.srw(a)gmail.com.

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Postdoc or PhD student in Computational Linguistics, Saarland University
by Alexander Koller 15 Dec '22

15 Dec '22

Research and teaching position (postdoc or PhD student) in computational linguistics HTML version with hyperlinks: https://www.coli.uni-saarland.de/~koller/page.php?id=jobs We are looking to fill a research and teaching position in computational linguistics at the Department of Language Science and Technology at Saarland University. The position is part of the research group of Prof. Alexander Koller, but offers great flexibility in developing your own research and teaching agenda, and collaborations with other research groups are encouraged. The position is flexible with respect to topic, but it should connect thematically with current topics of interest to the research group. These include syntactic and semantic parsing, semantics, dialogue, and natural language generation; themes of particular interest are currently user adaptation in interactive NLP and neurosymbolic models. You should have expertise in neural and/or linguistically principled methods in computational linguistics and be willing to take an active role in shaping the research and teaching environment of the department. The position includes a teaching load of up to four hours per week in the BSc Computational Linguistics (in German) and/or the MSc Language Science and Technology (in English). Both programs attract excellent and highly motivated students; it is not unusual for our students to publish papers at peer-reviewed conferences before graduation. The MSc students in particular are a very international crowd, with two thirds joining us from abroad. You will typically teach two seminars per semester on topics of your choice, which will allow you to motivate students to do BSc and MSc theses under your supervision. This is a position on the German TV-L E13 scale (100% position at the postdoc level; 75% position at the PhD student level). The starting salary of a 100% TV-L E13 position is a bit over 50,000 Euros per year and increases with experience. The initial appointment will be for three years; the position can be extended up to the limits of the German law for academic contracts (WissZeitVG). The starting date is April 1 or later: We want to fill the position in 2023, but could leave it open beyond April for the ideal candidate. Requirements We are looking for candidates who have finished, or are about to complete, an excellent PhD degree (at the postdoc level) or MSc degree (at the PhD student level) in computational linguistics, computer science, or a related discipline. You must be proficient in English (spoken and written); the ability to teach in German is a plus. Priority will be given to applicants at the postdoc level, who should have demonstrated their research expertise through high-quality publications. However, we will also consider applicants at the PhD student level. About the department Saarland University is one of the leading centers for computational linguistics in Europe, and offers a dynamic and stimulating research environment. The Department of Language Science and Technology consists of about 100 research staff in nine research groups in the fields of computational linguistics, psycholinguistics, speech processing, and corpus linguistics. The department is the centerpiece of the Collaborative Research Center 1102 "Information Density and Linguistic Encoding". It is part of the Saarland Informatics Campus, which brings together computer science research at the university with world-class research institutions on campus, such as the Max Planck Institute for Informatics, the Max Planck Institute for Software Systems, and the German Research Center for Artificial Intelligence (DFKI). The Saarland Informatics Campus brings together 900 researchers and 2100 students from 81 countries; SIC faculty have won 36 ERC grants. Saarland University is located in Saarbrücken, a mid-sized city in the tri-border area of Germany, France, and Luxembourg. Saarbrücken combines a lively culture scene with a relaxed atmosphere, and is quite an affordable place to live in. Our department maintains an international and diverse work environment. The primary working language is English; learning German while you are here will make it easier to connect with the local culture, but is not necessary for your work. How to apply Please submit your application at http://apply.coli.uni-saarland.de/ak22. Preference will be given to applications received by 22 January 2023. Include a single PDF file with the following information: • a statement of research interests that motivates why you are applying for this position and outlines your research agenda; • a full CV including your list of publications; • scans of transcripts and academic degree certificates; • the names, affiliations, and e-mail addresses of two people who can provide letters of reference for you. Saarland University especially welcomes applications from women and people with disabilities. If you have further questions, please email Alexander Koller <koller(a)coli.uni-saarland.de>. Applications should _not_ be emailed to this address, but submitted through the online form.

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CfP deadline update: Workshop @ICHL26 on Computational models of diachronic language change
by Stefania Degaetano-Ortlieb 15 Dec '22

15 Dec '22

Call for abstracts Workshop: Computational models of diachronic language change @the International Conference on Historical Linguistics (ICHL26) Organizers: Stefania Degaetano-Ortlieb*, Lauren Fonteyn+, Marie-Pauline Krielke*, Elke Teich* *Saarland University, +Leiden University Submission deadline: January 10th 2023 Submission format: One-page abstract (plus references) to be sent to s.degaetano(a)mx.uni-saarland.de <mailto:s.degaetano@mx.uni-saarland.de> Notification of acceptance: January 12th 2023 Additional information: We envisage a full day workshop in presence with presentations (20min + 5-10 min discussion). After the workshop, we aim to publish a Special Journal Issue in an open access journal. Workshop description: While the study of diachronic language change has long been firmly grounded in corpus data analysis, it seems fair to state that the field has been subject of a computational turn over the last decade or so, computational models being increasingly adopted across several research communities, including corpus and computational linguistics, computational social science, digital humanities, and historical linguistics. The core technique for the investigation of diachronic change are distributional models (DMs). DMs rely on the fact that related meanings occur in similar contexts and allow us to study lexical-semantic change in a data-driven way (e.g. as argued by Sagi et al. 2011), and on a larger scale (e.g. as shown on the Google NGram corpus by Gulordava & Baroni 2011). Besides count-based models (e.g. Hilpert & Saavedra 2017), contextualized word embeddings are increasingly employed for diachronic modeling, as such models are able to encode rich, context-sensitive information on word usage (see Lenci 2018 or Fonteyn et al., 2022 for discussion). In previous work, DMs have been used to determine laws of semantic change (e.g. Hamilton et al. 2016b, Dubossarsky et al. 2017) as well as develop statistical measures that help detect different types of change (e.g. specification vs. broadening; cultural change vs. linguistic change; Hamilton et al. 2016a, Del Tredici et al. 2019). DMs have also been used to map change in specific (groups of) concepts (e.g. racism, knowledge; see Sommerauer & Fokkens 2019 for a discussion). Further studies have suggested ways of improving the models that generate (diachronic) word embeddings to attain these goals (e.g. Rudolph & Blei 2018). Existing studies and projects focus on capturing and quantifying aspects of semantic change. Yet, over the past decade, DMs have also been shown to be useful to investigate other types of change in language use, including grammatical change. Within the computational and corpus linguistic communities, for example, Bizzoni et al. (2019, 2020) have shown an interdependency between lexical and grammatical changes and Teich et al. (2021) use embeddings to detect (lexico-) grammatical conventionalization (which may lead to grammaticalization). Within diachronic linguistics, the use of distributional models is focused on examining the underlying functions of grammatical structures across time (e.g. Perek 2016, Hilpert and Perek 2015, Gries and Hilpert 2008, Fonteyn 2020, Budts 2020). Specifically targeting historical linguistic questions, Rodda et al. (2019) and Sprugnoli et al. (2020) have shown that computational models are promising for analyzing ancient languages, and McGillivray et al. (2022) highlight the advantages of word embeddings (vs. count-based methods) while also pointing to the challenges and the limitations of these models. A common concern across these different communities is to better understand the general principles or laws of language change and the underlying mechanisms (analogy, priming, processing efficiency, contextual predictability as measured by surprisal, etc.). In the proposed workshop, we want to bring together researchers from relevant communities to talk about the unique promises that computational models hold when applied to diachronic data as well as the specific challenges they involve. In doing so, we will identify common ground and explore the most pressing problems and possible solutions. Specific questions will concern: Model utility: How can we capture change in language use beyond lexical-semantic change, e.g. change in grammatical constructions, collocations, phraseology? Model quality: How can we evaluate computational models of historical language stages in absence of native-speaker gold standards? To what extent does the quality of historical and diachronic corpora affect the performance of models? Model analytics: How do we transition from testing the reliability of models to employing them to address previously unanswered research questions on language change? How can we detect and measure change? What are suitable analytic procedures to interpret the output of models? References: Bizzoni, Y., Degaetano-Ortlieb, S., Menzel, K., Krielke, P., and Teich, E. (2019). Grammar and meaning: analysing the topology of diachronic word embeddings. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, ACL, Florence, Italy, pp. 175185. Bizzoni, Y., Degaetano-Ortlieb, S., Fankhauser, P., and Teich, E. (2020). Linguistic variation and change in 250 years of English scientific writing: a data-driven approach. Frontiers in Artificial Intelligence, 3. Budts, S. (2020). "A connectionist approach to analogy. On the modal meaning of periphrastic do in Early Modern English". Corpus Linguistics and Linguistic Theory, 18(2), pp. 337364. Del Tredici, M., Fernández, R., and Boleda, G. (2019). Short-term meaning shift: A distributional exploration. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): Human Language Technologies, Minneapolis, Minnesota, USA, pp. 20692075. Dubossarsky, H., Weinshall, D., and Grossman, E. (2017). Outta control: laws of semantic change and inherent biases in word representation models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, pp. 11361145. Fonteyn, L. (2020). "What about grammar? Using BERT embeddings to explore functional-semantic shifts of semi-lexical and grammatical constructions." Computational Humanities Research CEUR-WS, pp. 257268. Fonteyn, L., Manjavacas, E., and Budts, S. (2022). Exploring Morphosyntactic Variation & Change with Distributional Semantic Models. Journal of Historical Syntax, 7(12), pp. 141. Gries, S. T., and Hilpert, M. (2008). The identification of stages in diachronic data: variability-based Neighbor Clustering. Corpora, 3(1), pp. 5981. Gulordava, K., and Baroni, M. (2011). A distributional similarity approach to the detection of semantic change in the Google Books Ngram corpus. In Proceedings of Geometrical Models for Natural Language Semantics (GEMS), EMNLP, Edinburgh, United Kingdom, pp. 6771. Hamilton, W. L., Leskovec, J., and Jurafsky, D. (2016a). Cultural shift or linguistic drift? comparing two computational models of semantic change. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, Austin, Texas, USA, pp. 21162121. Hamilton, W. L., Leskovec J., and Jurafsky, D. (2016b). Diachronic word embeddings reveal statistical laws of semantic change. In Proceedings of Morphosyntactic Variation & Change with DSMs, 54th Annual Meeting of the Association for Computational Linguistics, ACL, Berlin, Germany, pp. 14891501. Hilpert, M., and Saavedra, D.C. (2020). "Using token-based semantic vector spaces for corpus-linguistic analyses: From practical applications to tests of theoretical claims". Corpus Linguistics and Linguistic Theory, 16(2), pp. 393424. Hilpert, M. and Perek, F. (2015). Meaning change in a petri dish: constructions, semantic vector spaces, and motion charts. Linguistics Vanguard, 1(1), pp. 339350. Lenci, A. (2018). Distributional Models of Word Meaning. Annual Review of Linguistics, 4, pp. 151171. McGillivray, B., Jenset, G.B., Salama, K. (2022. Investigating patterns of change, stability, and interaction among scientific disciplines using embeddings. Nature Humanities and Socical Science Communication 9, 285. Perek, F. (2016). Using distributional semantics to study syntactic productivity in diachrony: a case study. Linguistics, 54(1), pp. 149188. Rodda, M.A., Probert, P., and McGillivray, B. (2019). Vector space models of Ancient Greek word meaning, and a case study on Homer. TAL Traitement Automatique des Langues, 60(3), pp. 6387. Rudolph, M., and Blei, D. (2018). Dynamic embeddings for language evolution. In Proceedings of the 2018 World Wide Web Conference (WWW 18), Lyon, France, pp. 10031011. Sagi, E., Kaufmann, S., and Clark, B. (2011). Tracing semantic change with Latent Semantic Analysis. Current Methods in Historical Semantics, 73, pp. 161183. Sommerauer, P., and Fokkens, A. (2019). Conceptual Change and Distributional Semantic Models: An Exploratory Study on Pitfalls and Possibilities. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, Florence, Italy, pp. 223233. Sprugnoli, R., Moretti, G., and Passarotti, M. (2020). Building and Comparing Lemma Embeddings for Latin. Classical Latin versus Thomas Aquinas. IJCoL. Italian Journal of Computational Linguistics, 6(6-1), pp. 2945. Teich, E., Fankhauser P., Degaetano-Ortlieb, S., and Bizzoni, Y. (2021). Less is More/More Diverse: On the Communicative Utility of Linguistic Conventionalization. Frontiers in Communication, 5.

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Fully funded PhD in NLP, London (QMUL)
by Haim Dubossarsky 15 Dec '22

15 Dec '22

A fully funded 3.5-year PhD studentship in NLP on the topic of meaning change is offered at Queen Mary University of London (QMUL), School of Electronic Engineering and Computer Science. I am looking for brilliant candidates who hold (or about to hold) MSc in Computer Science with a strong NLP research background. Prospective students can learn more about the project here <http://eecs.qmul.ac.uk/phd/phd-studentships/principal-and-epsrc-dtp-phd-stu…>, under the section: *Next generation NLP methods for meaning change* . The PhD student will be supervised by me and co-supervised by Prof Maria Liakata, and will also be part of the Change is Key! <https://www.changeiskey.org/> international research program. For further details on the application process and eligibility criteria please read this <http://eecs.qmul.ac.uk/phd/phd-studentships/principal-and-epsrc-dtp-phd-stu…> . If you are interested, please get in touch with me on: h.dubossarsky(a)qmul.ac.uk. Bests, Haim

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Call for applications: junior and senior permanent research positions in the ALMAnacH team at Inria, Paris, France
by Benoît Sagot 14 Dec '22

14 Dec '22

Dear all, The ALMAnaCH lab at Inria Paris is looking for candidates for full-time permanent researchers in natural language processing (NLP) and digital humanities (DH) to join us in the beautiful city of Paris. We welcome profiles for both junior and senior positions. A PhD and at least one post-doctoral experience in NLP or ML for NLP are required for junior positions, whereas a longer track record is required for senior positions. Inria is the French national research institute for computer science and applied mathematics. World-class research, and, where relevant, transfer to industry and start-up creation are its main missions. The ALMAnaCH lab focuses on NLP and DH, at the crossroads between theoretical computer science, machine and deep learning, and linguistics. More about ALMAnaCH: http://almanach.inria.fr/index-en.html — also, you can follow @InriaParisNLP on Twitter and Mastodon. Working at ALMAnaCH means working in a diverse and inclusive team within an exceptional scientific environment, next to other leading Inria research teams in Computer Vision, Machine Learning and more. Upon arrival, junior permanent researchers will receive a welcome package including a full PhD grant and expenses. You can find more information here: http://almanach.inria.fr/recruitment-en.html. If you are interested, please contact me by the end of the year at benoit.sagot<αt>inria.fr. All the best, Benoît Sagot Directeur de Recherches Inria Head of the ALMAnaCH team (Inria Paris research center)

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Job offer: postdoctoral researcher in Natural Language Processing, a Universidade da Coruña (Spain)
by Carlos Gómez Rodríguez 14 Dec '22

14 Dec '22

We are looking for candidates for a full-time postdoctoral researcher position at the University of A Coruña, within the research project "SALSA – efficient Syntactic Analysis for Large-scale Sentiment Analysis", funded by an ERC Proof-of-Concept Grant and led by the Prof. Carlos Gomez-Rodriguez. The main objective of the project is to apply the efficient parsers developed in the ERC Starting Grant project "FASTPARSE – Fast Natural Language Parsing for Large-Scale NLP" (https://fastparse.grupolys.org) to sentiment analysis, creating systems that can be useful for small entities without large computational resources. The selected person would join the LyS research group, one of the most active in natural language processing in Europe, and would receive a salary of € 40,880.04 gross per year. The expected start date would be February 1, 2023, and the project will end on July 31, 2024. More detailed information on the offer (nature of the job, information about the group, working conditions, requirements and instructions for applying) can be found at https://fastparse.grupolys.org/job-vacancies . The formal application deadline expires on 20th December, but for administrative reasons, applications should be sent to the PI by the 19th. Those interested are encouraged to contact the PI, Carlos Gómez-Rodríguez (cgomezr(a)udc.es) as soon as possible to apply or to ask any questions about a potential application.

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Assistant Professor, Humanities Data Science, University of Washington
by Gina-Anne Levow 14 Dec '22

14 Dec '22

Assistant Professor of Humanities Data SciencesPosition Description The Division of the Humanities in the College of Arts and Sciences at the University of Washington, Seattle, WA, USA invites applications for a full-time, tenure-track Assistant Professor position in Non-Anglophone Humanities Data Science. The successful candidate will be appointed to one of the eight departments in the Division: Asian Languages and Literature, French and Italian Studies, German Studies, Linguistics, Middle Eastern Languages and Cultures, Scandinavian Studies, Slavic Languages and Literatures, and Spanish and Portuguese Studies. The successful candidate for this position, researching and teaching primarily in one or several languages other than English, will be expected to : - create, analyze, use, present and communicate data, broadly understood, in disciplines of language, literature, and/or culture - explore a variety of forms of data including large historical or contemporary corpora, textual archives, digital media and genres, or online datasets and their uses - engage a range of analytical techniques and methods of analysis - investigate questions and challenges posed by data science and by data, such as the role of bias in algorithms, uncertainty in artificial intelligence, and/or the use of ever-larger datasets in machine translation - engage in an active research agenda Since this is a cross-disciplinary position, the successful applicant will also be expected to work across departmental and disciplinary boundaries, and contribute to the development of existing cross-disciplinary initiatives (e.g., Translation Studies Hub <https://urldefense.com/v3/__https://uwtranslationhub.wixsite.com/uwtranslat…> , Textual and Digital Studies <http://txtds.uw.edu/>, Global Literary Studies <https://slavic.washington.edu/fields/global-literary-studies>) and to new curricula and programs in areas of inquiry such as human and machine translation, computational text analysis, AI and creativity, the future of language learning. In particular, this position has been created in conjunction with the establishment of a new minor in Data Science intended for Social Science and Humanities students ( http://www.washington.edu/uaa/advising/single-pages/data-science-minor/). The candidate hired into this position will dedicate 25% of their teaching and service responsibilities to this program, with the remainder determined within the context of the appointing department. The successful candidate will be expected to take a leading role in the minor, including developing and teaching courses in the required Data Skills and Data Studies categories, and serving on the Steering and Curriculum Committees of the Data Science minor. Candidates should in addition be prepared to take full advantage of the rich array of resources at the UW for research and teaching in data science as well as in the digital humanities, including the <https://urldefense.com/v3/__https://simpsoncenter.org/programs/digital-huma…>Simpson Center for the Humanities <https://urldefense.com/v3/__https://simpsoncenter.org/programs/digital-huma…>, the <https://escience.washington.edu/>eScience Institute <https://escience.washington.edu/>, the <https://www.lib.washington.edu/openscholarship>Open Scholarship Commons <https://www.lib.washington.edu/openscholarship> in the UW Libraries, and the Humanities Data Lab <http://humanitiesdatalab.ds.lib.uw.edu/>. The candidate is also expected to actively contribute to the diversity, equity and inclusivity goals of their potential department, the Humanities Division, and the University (as articulated in departmental statements and in the UW Diversity Blueprint: <http://www.washington.edu/diversity/diversity-blueprint/> http://www.washington.edu/diversity/diversity-blueprint/). University of Washington faculty engage in teaching, research and service. This position has an anticipated start date of September 2023, and will have a 9-month service period. Qualifications Applicants must, by the start of the appointment, have a PhD, or foreign equivalent, in a field consistent with an appointment in a department in the Division of the Humanities. The successful candidate must have a record of innovative and effective teaching and student mentoring; and a record of contribution to departmental or institutional diversity, equity and inclusion initiatives. Instructions Priority will be given to applicants who submit the following materials by January 9 at https://ap.washington.edu/ahr/position-details/?job_id=107708: - A cover letter that describes the candidate’s suitability for this position and explains how the candidate’s experiences, activities, and goals will lead to success in a cross-disciplinary position - CV - Research statement, not to exceed one page, describing present projects and future directions - A representative example of research (such as a chapter, article, or a link to an online project or a digital tool) - A teaching portfolio, which should provide evidence of teaching excellence and may include such items as: example syllabi, lecture videos, website links for courses or student activities, or digital tools created by the candidate - A statement, not to exceed one page, on how their work would contribute to the diversity, equity and inclusivity goals of their department, the Humanities Division, and the University (as articulated in the UW Diversity Blueprint: <http://www.washington.edu/diversity/diversity-blueprint/> http://www.washington.edu/diversity/diversity-blueprint/) - Three letters of recommendation Because applicants are expected to come from a variety of disciplinary orientations, traditional and nontraditional backgrounds, resulting in different professional profiles, applicants are welcomed in any of the above materials to provide details that can help to contextualize their dossier. Such details might include experiences, aspects of training, outreach, teaching or research that are felt to contribute to a distinctive professional profile. Contact Prof. Gina-Anne Levow for questions regarding this position (levow(a)uw.edu).

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2nd Call for Abstracts (Extended Deadline) - 2023 NARNiHS Research Incubator
by Lauersdorf, Mark R. 13 Dec '22

13 Dec '22

*** *Second* Call for Abstracts and *Extended Deadline* *** 2023 NARNiHS Research Incubator *** North American Research Network in Historical Sociolinguistics *** 5th edition 20-22 April 2023 - entirely online! The 2023 NARNiHS Research Incubator will take place as an entirely online event (with free registration). This presents a great opportunity for scholars in historical sociolinguistics from all over the world to participate as presenters and/or attendees without the limitations imposed by international travel, and we encourage our fellow historical sociolinguists, and scholars from related fields, from our global scholarly community (in addition to North America), to join us online for our Research Incubator this spring. ==> NEW Abstract submission deadline: ==> 15 January 2023, 11:59 PM (U.S. Eastern Time). ==> Abstract submission online: ==> http://linguistlist.org/easyabs/NARNiHS2023_RI . The North American Research Network in Historical Sociolinguistics (NARNiHS) is accepting abstracts for its 2023 NARNiHS Research Incubator. Building on the great success of the first four years, the 5th edition of this unique kind of NARNiHS conference seeks to provide a collaborative environment where presenters bring work that is in-progress, exploratory, proof-of-concept, prototyping; and the audience actively participates in the brainstorming and workshopping of those new ideas. We see the NARNiHS Research Incubator as a place for testing/pushing boundaries; developing new theories, methods, models, tools; seeking feedback from peers willing to engage in productive assessment of fledgling ideas and nascent projects. Successful abstracts for this research incubator environment will demonstrate thorough grounding in the field, scientific rigor in the formulation of research questions, and promise for rich discussion of ideas. NARNiHS welcomes papers in all areas of historical sociolinguistics, which is understood as the application/development of sociolinguistic theories, methods, and models for the study of historical language variation and change over time, or more broadly, the study of the interaction of language and society in historical periods and from historical perspectives. Thus, a wide range of linguistic areas, subdisciplines, and methodologies easily find their place within the field, and we encourage submission of abstracts that reflect this broad scope. We are soliciting abstracts for 25-minute presentations. Presenters will have the entire 25 minutes for their presentations, with discussion happening in the "incubation session" at the end of each panel. Abstracts should be no more than one page (not including examples and references, see below). Abstracts will be accepted until 19 December 2022 - late abstracts will not be considered. Successful abstracts will be explicit about which theoretical frameworks, methodological protocols, and analytical strategies are being applied or critiqued; and data sources and examples should be sufficiently (if briefly) presented, so as to allow reviewers a full understanding of the scope and claims of the research. Please note that the connection of your research to the field of historical sociolinguistics should be explicitly outlined in your abstract. Failure to adhere to these criteria will likely result in non-acceptance. To encourage maximum exchange of ideas in the brainstorming/workshopping environment of the NARNiHS Research Incubator, presentations will be grouped into thematic panels of three presentations, each panel followed by an hour-long discussion with the audience led by specialists. Discussion will encompass specific feedback on the individual papers as well as consideration of overarching questions of theory, methods, and models emerging from the papers. To facilitate such discussion, authors will be required to submit a draft of their presentation materials for distribution to the panel discussants and to the other presenters 10 days prior to the start of the conference. General Requirements: 1) Abstracts must be submitted electronically, using the following link: http://linguistlist.org/easyabs/NARNiHS2023_RI 2) Papers must be delivered as projected in the abstract or represent bona fide developments of the same research. 3) Authors are expected to virtually attend the conference and present their own papers. 4) Presentations will be delivered via a video-conferencing platform, most likely Zoom. Technical details and instructions regarding the platform for our NARNiHS Research Incubator will be sent to authors in due time. Content Requirements: 1) Abstracts should be explicit about which theoretical frameworks, methodological protocols, and analytical strategies are being applied or critiqued. 2) Data sources and examples should be sufficiently (if briefly) presented, so as to allow reviewers a full understanding of the scope and claims of the research. 3) The connection of your research to the field of historical sociolinguistics should be explicitly outlined. Abstract Format Guidelines: 1) Abstracts must be submitted in PDF format. 2) Abstracts must fit on one standard 8.5×11 inch page, with margins no smaller than 1 inch and a font style and size no smaller than Times New Roman 12 point. All additional content (visualizations, trees, tables, figures, captions, examples, and references) must fit on a single (1) additional page. No exceptions to these requirements are allowed. 3) Anonymize your abstract. We realize that sometimes it is not possible to attain complete anonymity, but there is a difference between "inability to anonymize completely" (due to the nature of the research) and "careless non-anonymizing" (for example: "In Jones 2021, I describe..."). In addition, be sure to anonymize your PDF file (you may do so in Adobe Acrobat Reader by clicking on "File", then "Properties", removing your name if it appears in the "Author" line of the "Description" tab, and re-saving before submitting it). Please be aware that abstract file names might not be automatically anonymized by the system; do not use your name (e.g. Smith_Abstract.pdf) when saving your abstract in PDF format, rather, use non-identifying information (e.g. HistSoc4Lyfe_NARNiHS.pdf). Your name should only appear in the online form accompanying your abstract submission. Papers that are not sufficiently anonymized wherever possible (whether in the text of the abstract or in the metadata of the digital file) risk being rejected. Please contact us at NARNiHistSoc(a)gmail.com with any questions.

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CfP Workshop @ICHL26 on Computational models of diachronic language change
by Stefania Degaetano-Ortlieb 13 Dec '22

13 Dec '22

Call for abstracts Workshop: Computational models of diachronic language change @the International Conference on Historical Linguistics (ICHL26) Organizers: Stefania Degaetano-Ortlieb*, Lauren Fonteyn+, Marie-Pauline Krielke*, Elke Teich* *Saarland University, +Leiden University Submission deadline: January 1st 2023 Submission format: One-page abstract (plus references) to be sent to s.degaetano(a)mx.uni-saarland.de <mailto:s.degaetano@mx.uni-saarland.de> Notification of acceptance: January 12th 2023 Additional information: We envisage a full day workshop in presence with presentations (20min + 5-10 min discussion). After the workshop, we aim to publish a Special Journal Issue in an open access journal. Workshop description: While the study of diachronic language change has long been firmly grounded in corpus data analysis, it seems fair to state that the field has been subject of a computational turn over the last decade or so, computational models being increasingly adopted across several research communities, including corpus and computational linguistics, computational social science, digital humanities, and historical linguistics. The core technique for the investigation of diachronic change are distributional models (DMs). DMs rely on the fact that related meanings occur in similar contexts and allow us to study lexical-semantic change in a data-driven way (e.g. as argued by Sagi et al. 2011), and on a larger scale (e.g. as shown on the Google NGram corpus by Gulordava & Baroni 2011). Besides count-based models (e.g. Hilpert & Saavedra 2017), contextualized word embeddings are increasingly employed for diachronic modeling, as such models are able to encode rich, context-sensitive information on word usage (see Lenci 2018 or Fonteyn et al., 2022 for discussion). In previous work, DMs have been used to determine laws of semantic change (e.g. Hamilton et al. 2016b, Dubossarsky et al. 2017) as well as develop statistical measures that help detect different types of change (e.g. specification vs. broadening; cultural change vs. linguistic change; Hamilton et al. 2016a, Del Tredici et al. 2019). DMs have also been used to map change in specific (groups of) concepts (e.g. racism, knowledge; see Sommerauer & Fokkens 2019 for a discussion). Further studies have suggested ways of improving the models that generate (diachronic) word embeddings to attain these goals (e.g. Rudolph & Blei 2018). Existing studies and projects focus on capturing and quantifying aspects of semantic change. Yet, over the past decade, DMs have also been shown to be useful to investigate other types of change in language use, including grammatical change. Within the computational and corpus linguistic communities, for example, Bizzoni et al. (2019, 2020) have shown an interdependency between lexical and grammatical changes and Teich et al. (2021) use embeddings to detect (lexico-) grammatical conventionalization (which may lead to grammaticalization). Within diachronic linguistics, the use of distributional models is focused on examining the underlying functions of grammatical structures across time (e.g. Perek 2016, Hilpert and Perek 2015, Gries and Hilpert 2008, Fonteyn 2020, Budts 2020). Specifically targeting historical linguistic questions, Rodda et al. (2019) and Sprugnoli et al. (2020) have shown that computational models are promising for analyzing ancient languages, and McGillivray et al. (2022) highlight the advantages of word embeddings (vs. count-based methods) while also pointing to the challenges and the limitations of these models. A common concern across these different communities is to better understand the general principles or laws of language change and the underlying mechanisms (analogy, priming, processing efficiency, contextual predictability as measured by surprisal, etc.). In the proposed workshop, we want to bring together researchers from relevant communities to talk about the unique promises that computational models hold when applied to diachronic data as well as the specific challenges they involve. In doing so, we will identify common ground and explore the most pressing problems and possible solutions. Specific questions will concern: Model utility: How can we capture change in language use beyond lexical-semantic change, e.g. change in grammatical constructions, collocations, phraseology? Model quality: How can we evaluate computational models of historical language stages in absence of native-speaker gold standards? To what extent does the quality of historical and diachronic corpora affect the performance of models? Model analytics: How do we transition from testing the reliability of models to employing them to address previously unanswered research questions on language change? How can we detect and measure change? What are suitable analytic procedures to interpret the output of models? References: Bizzoni, Y., Degaetano-Ortlieb, S., Menzel, K., Krielke, P., and Teich, E. (2019). Grammar and meaning: analysing the topology of diachronic word embeddings. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, ACL, Florence, Italy, pp. 175185. Bizzoni, Y., Degaetano-Ortlieb, S., Fankhauser, P., and Teich, E. (2020). Linguistic variation and change in 250 years of English scientific writing: a data-driven approach. Frontiers in Artificial Intelligence, 3. Budts, S. (2020). "A connectionist approach to analogy. On the modal meaning of periphrastic do in Early Modern English". Corpus Linguistics and Linguistic Theory, 18(2), pp. 337364. Del Tredici, M., Fernández, R., and Boleda, G. (2019). Short-term meaning shift: A distributional exploration. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): Human Language Technologies, Minneapolis, Minnesota, USA, pp. 20692075. Dubossarsky, H., Weinshall, D., and Grossman, E. (2017). Outta control: laws of semantic change and inherent biases in word representation models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, pp. 11361145. Fonteyn, L. (2020). "What about grammar? Using BERT embeddings to explore functional-semantic shifts of semi-lexical and grammatical constructions." Computational Humanities Research CEUR-WS, pp. 257268. Fonteyn, L., Manjavacas, E., and Budts, S. (2022). Exploring Morphosyntactic Variation & Change with Distributional Semantic Models. Journal of Historical Syntax, 7(12), pp. 141. Gries, S. T., and Hilpert, M. (2008). The identification of stages in diachronic data: variability-based Neighbor Clustering. Corpora, 3(1), pp. 5981. Gulordava, K., and Baroni, M. (2011). A distributional similarity approach to the detection of semantic change in the Google Books Ngram corpus. In Proceedings of Geometrical Models for Natural Language Semantics (GEMS), EMNLP, Edinburgh, United Kingdom, pp. 6771. Hamilton, W. L., Leskovec, J., and Jurafsky, D. (2016a). Cultural shift or linguistic drift? comparing two computational models of semantic change. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, Austin, Texas, USA, pp. 21162121. Hamilton, W. L., Leskovec J., and Jurafsky, D. (2016b). Diachronic word embeddings reveal statistical laws of semantic change. In Proceedings of Morphosyntactic Variation & Change with DSMs, 54th Annual Meeting of the Association for Computational Linguistics, ACL, Berlin, Germany, pp. 14891501. Hilpert, M., and Saavedra, D.C. (2020). "Using token-based semantic vector spaces for corpus-linguistic analyses: From practical applications to tests of theoretical claims". Corpus Linguistics and Linguistic Theory, 16(2), pp. 393424. Hilpert, M. and Perek, F. (2015). Meaning change in a petri dish: constructions, semantic vector spaces, and motion charts. Linguistics Vanguard, 1(1), pp. 339350. Lenci, A. (2018). Distributional Models of Word Meaning. Annual Review of Linguistics, 4, pp. 151171. Perek, F. (2016). Using distributional semantics to study syntactic productivity in diachrony: a case study. Linguistics, 54(1), pp. 149188. Rodda, M.A., Probert, P., and McGillivray, B. (2019). Vector space models of Ancient Greek word meaning, and a case study on Homer. TAL Traitement Automatique des Langues, 60(3), pp. 6387. Rudolph, M., and Blei, D. (2018). Dynamic embeddings for language evolution. In Proceedings of the 2018 World Wide Web Conference (WWW 18), Lyon, France, pp. 10031011. Sagi, E., Kaufmann, S., and Clark, B. (2011). Tracing semantic change with Latent Semantic Analysis. Current Methods in Historical Semantics, 73, pp. 161183. Sommerauer, P., and Fokkens, A. (2019). Conceptual Change and Distributional Semantic Models: An Exploratory Study on Pitfalls and Possibilities. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, Florence, Italy, pp. 223233. Sprugnoli, R., Moretti, G., and Passarotti, M. (2020). Building and Comparing Lemma Embeddings for Latin. Classical Latin versus Thomas Aquinas. IJCoL. Italian Journal of Computational Linguistics, 6(6-1), pp. 2945. Teich, E., Fankhauser P., Degaetano-Ortlieb, S., and Bizzoni, Y. (2021). Less is More/More Diverse: On the Communicative Utility of Linguistic Conventionalization. Frontiers in Communication, 5.

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Tenure-Track position in computational linguistics at Brandeis University
by Nianwen Xue 13 Dec '22

13 Dec '22

*Tenure-track Assistant Professor, Computational Linguistics* The Department of Computer Science at Brandeis University invites applications for a tenure-track assistant professor in computational linguistics, beginning Fall 2023. Qualifications required of all applicants include a Ph.D., in hand by Fall 2023, in Computer Science or a related discipline, a strong research record, and a commitment to teaching at the undergraduate and graduate levels. Particular attention will be given to candidates pursuing research in the broad area of speech, dialogue, or multimodal language processing. This position is subject to budgetary approval. The Department consists of a diverse group of 20 full-time faculty members and researchers and offers programs leading to B.A./B.S., M.S., and Ph.D. degrees in Computer Science and an M.S. in Computational Linguistics. The Department has research strengths in computational linguistics, theoretical linguistics, machine learning, computer vision, data mining, networking, distributed systems, operating systems, databases, algorithms, and software design and implementation. In addition, members of the Department collaborate closely with faculty across the university including biology, neuroscience, economics, physics, political science, among others. At Brandeis, we believe that diversity, equity, and inclusion are essential components of academic excellence. Brandeis University is an affirmative action, equal opportunity employer that is committed to creating equitable access and opportunities for applicants to all employment positions. Because diversity, equity, and inclusion are at the core of Brandeis’ history and mission, we value and are seeking candidates that represent a variety of social identities, including those that have been underrepresented in higher education, who possess skills that spark innovation, and who, through their scholarly pursuits, teaching, and/or service experiences, bring expertise in building, engaging, and sustaining a pluralistic, just, and inclusive campus community. Applicants should submit a CV, research statement, and teaching statement, and arrange for at least three reference letters to be submitted to AcademicJobsOnline. Because Brandeis is committed to advancing diversity, equity, and inclusion in all areas of faculty effort, applicants should address at least one of these areas in their cover letter and/or the teaching statement. Qualified applicants should apply at https://academicjobsonline.org/ajo/jobs/22659 . First consideration will be given to applications received by December 20, 2022. Questions about the position can be directed to Professor Nianwen Xue at xuen(a)brandeis.edu. Additional information about the Department is available at https://www.brandeis.edu/computer-science <https://www.brandeis.edu/computer-science/,>, and information about the Computational Linguistics program is available at https://www.brandeis.edu/computer-science/computational-linguistics/index.h… .

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