Abstract
The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science. This is commonly approached by training word embeddings on each corpus, aligning the vector spaces, and looking for words whose cosine distance in the aligned space is large. However, these methods often require extensive filtering of the vocabulary to perform well, and-as we show in this work-result in unstable, and hence less reliable, results. We propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. The method is simple, interpretable and stable. We demonstrate its effectiveness in 9 different setups, considering different corpus splitting criteria (age, gender and profession of tweet authors, time of tweet) and different languages (English, French and Hebrew).
Original language | English |
---|---|
Title of host publication | ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 538-555 |
Number of pages | 18 |
ISBN (Electronic) | 9781952148255 |
State | Published - 2020 |
Event | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: 5 Jul 2020 → 10 Jul 2020 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
---|---|
ISSN (Print) | 0736-587X |
Conference
Conference | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 |
---|---|
Country/Territory | United States |
City | Virtual, Online |
Period | 5/07/20 → 10/07/20 |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics
Funding
We thank Marianna Apidianiaki for her insightful comments on an earlier version of this work. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT), and from the the Israeli ministry of Science, Technology and Space through the Israeli-French Mai-monide Cooperation programme. The second and third authors were partially funded by the French Research Agency projects ParSiTi (ANR-16-CE33-0021), SoSweet (ANR15-CE38-0011-01) and by the French Ministry of Industry and Ministry of Foreign Affairs via the PHC Maimonide France-Israel cooperation programme. We thank Marianna Apidianiaki for her insightful comments on an earlier version of this work. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT), and from the the Israeli ministry of Science, Technology and Space through the Israeli-French Maimonide Cooperation programme. The second and third authors were partially funded by the French Research Agency projects ParSiTi (ANR-16-CE33-0021), SoSweet (ANR15-CE38-0011-01) and by the French Ministry of Industry and Ministry of Foreign Affairs via the PHC Maimonide France-Israel cooperation programme.
Funders | Funder number |
---|---|
French Ministry of Industry | |
Israeli-French Mai-monide Cooperation programme | |
Israeli-French Maimonide Cooperation programme | |
Horizon 2020 Framework Programme | |
Providence Health Care | |
European Commission | |
Agence Nationale de la Recherche | ANR-16-CE33-0021, ANR15-CE38-0011-01 |
Ministry of Science, Technology and Space | |
Ministry of Foreign Affairs | |
Horizon 2020 | 802774 |