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Publications (3 of 3) Show all publications
Ryazanov, I. & Björklund, J. (2024). Thesis Proposal: Detecting Agency Attribution. In: Neele Falk; Sara Papi; Mike Zhang (Ed.), Proceedings of the 18th conference of the European chapter of the association for computational linguistics: student research workshop. Paper presented at 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024, St. Julian’s, Malta, March 17-22, 2024 (pp. 208-214). Association for Computational Linguistics (ACL)
Open this publication in new window or tab >>Thesis Proposal: Detecting Agency Attribution
2024 (English)In: Proceedings of the 18th conference of the European chapter of the association for computational linguistics: student research workshop / [ed] Neele Falk; Sara Papi; Mike Zhang, Association for Computational Linguistics (ACL) , 2024, p. 208-214Conference paper, Published paper (Refereed)
Abstract [en]

We explore computational methods for perceived agency attribution in natural language data. We consider ‘agency’ as the freedom and capacity to act, and the corresponding Natural Language Processing (NLP) task involves automatically detecting attributions of agency to entities in text. Our theoretical framework draws on semantic frame analysis, role labelling and related techniques. In initial experiments, we focus on the perceived agency of AI systems. To achieve this, we analyse a dataset of English-language news coverage of AI-related topics, published within one year surrounding the release of the Large Language Model-based service ChatGPT, a milestone in the general public’s awareness of AI. Building on this, we propose a schema to annotate a dataset for agency attribution and formulate additional research questions to answer by applying NLP models.

Place, publisher, year, edition, pages
Association for Computational Linguistics (ACL), 2024
National Category
Language Technology (Computational Linguistics) Computer Sciences
Identifiers
urn:nbn:se:umu:diva-222874 (URN)2-s2.0-85188728107 (Scopus ID)9798891760905 (ISBN)
Conference
18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024, St. Julian’s, Malta, March 17-22, 2024
Funder
Marianne and Marcus Wallenberg FoundationWallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2024-04-16 Created: 2024-04-16 Last updated: 2024-04-16Bibliographically approved
Devinney, H., Eklund, A., Ryazanov, I. & Cai, J. (2023). Developing a multilingual corpus of wikipedia biographies. In: Ruslan Mitkov; Maria Kunilovskaya; Galia Angelova (Ed.), International conference. Recent advances in natural language processing 2023, large language models for natural language processing: proceedings. Paper presented at 14th international conference on Recent Advances in Natural Language Processing 2023, Varna, Bulgaria, September 4-6, 2023.. Shoumen, Bulgaria: Incoma ltd., Article ID 2023.ranlp-1.32.
Open this publication in new window or tab >>Developing a multilingual corpus of wikipedia biographies
2023 (English)In: International conference. Recent advances in natural language processing 2023, large language models for natural language processing: proceedings / [ed] Ruslan Mitkov; Maria Kunilovskaya; Galia Angelova, Shoumen, Bulgaria: Incoma ltd. , 2023, article id 2023.ranlp-1.32Conference paper, Published paper (Refereed)
Abstract [en]

For many languages, Wikipedia is the mostaccessible source of biographical information. Studying how Wikipedia describes the lives ofpeople can provide insights into societal biases, as well as cultural differences more generally. We present a method for extracting datasetsof Wikipedia biographies. The accompanying codebase is adapted to English, Swedish, Russian, Chinese, and Farsi, and is extendable to other languages. We present an exploratory analysis of biographical topics and gendered patterns in four languages using topic modelling and embedding clustering. We find similarities across languages in the types of categories present, with the distribution of biographies concentrated in the language’s core regions. Masculine terms are over-represented and spread out over a wide variety of topics. Feminine terms are less frequent and linked to more constrained topics. Non-binary terms are nearly non-represented.

Place, publisher, year, edition, pages
Shoumen, Bulgaria: Incoma ltd., 2023
Series
International conference Recent advances in natural language processing, ISSN 2603-2813 ; 2023
National Category
Language Technology (Computational Linguistics)
Research subject
computational linguistics
Identifiers
urn:nbn:se:umu:diva-213781 (URN)10.26615/978-954-452-092-2_032 (DOI)2-s2.0-85179178058 (Scopus ID)978-954-452-092-2 (ISBN)
Conference
14th international conference on Recent Advances in Natural Language Processing 2023, Varna, Bulgaria, September 4-6, 2023.
Available from: 2023-11-10 Created: 2023-11-10 Last updated: 2023-12-22Bibliographically approved
Ryazanov, I. & Björklund, J. (2023). How does the language of 'threat' vary across news domains?: a semi-supervised pipeline for understanding narrative components in news contexts. In: Håkan Grahn; Anton Borg; Martin Boldt (Ed.), SAIS 2023: 35th Annual Workshop of the Swedish Artificial Intelligence Society. Paper presented at SAIS 2023, 35th Annual Workshop of the Swedish Artificial Intelligence Society, Karlskrona, Sweden, June 12-13, 2023 (pp. 94-99). Swedish Artificial Intelligence Society
Open this publication in new window or tab >>How does the language of 'threat' vary across news domains?: a semi-supervised pipeline for understanding narrative components in news contexts
2023 (English)In: SAIS 2023: 35th Annual Workshop of the Swedish Artificial Intelligence Society / [ed] Håkan Grahn; Anton Borg; Martin Boldt, Swedish Artificial Intelligence Society , 2023, p. 94-99Conference paper, Published paper (Refereed)
Abstract [en]

By identifying and characterising the narratives told in news media we can better understand political and societal processes. The problem is challenging from the perspective of natural language processing because it requires a combination of quantitative and qualitative methods. This paper reports on work in progress, which aims to build a human-in-the-loop pipeline for analysing how the variation of narrative themes across different domains, based on topic modelling and word embeddings. As an illustration, we study the language associated with the threat narrative in British news media.

Place, publisher, year, edition, pages
Swedish Artificial Intelligence Society, 2023
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740
Keywords
topic modelling, natural language processing, narrative analysis, text embeddings
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
computational linguistics; Computer Science
Identifiers
urn:nbn:se:umu:diva-213801 (URN)10.3384/ecp199010 (DOI)978-91-8075-274-9 (ISBN)
Conference
SAIS 2023, 35th Annual Workshop of the Swedish Artificial Intelligence Society, Karlskrona, Sweden, June 12-13, 2023
Available from: 2023-08-29 Created: 2023-08-29 Last updated: 2023-08-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4466-1567

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