{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T14:18:15Z","timestamp":1764857895204,"version":"3.41.0"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"crossref","award":["139943784 DFG"],"award-info":[{"award-number":["139943784 DFG"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM\/IMS Trans. Data Sci."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>\n            During the past 15 years, automatic text scaling has become one of the key tools of the Text as Data community in political science. Prominent text-scaling algorithms, however, rely on the assumption that latent positions can be captured just by leveraging the information about word frequencies in documents under study. We challenge this traditional view and present a new, semantically aware text-scaling algorithm,\n            <jats:italic>SemScale<\/jats:italic>\n            , which combines recent developments in the area of computational linguistics with unsupervised graph-based clustering. We conduct an extensive quantitative analysis over a collection of speeches from the European Parliament in five different languages and from two different legislative terms, and we show that a scaling approach relying on semantic document representations is often better at capturing known underlying political dimensions than the established frequency-based (i.e., symbolic) scaling method. We further validate our findings through a series of experiments focused on text preprocessing and feature selection, document representation, scaling of party manifestos, and a supervised extension of our algorithm. To catalyze further research on this new branch of text-scaling methods, we release a Python implementation of\n            <jats:italic>SemScale<\/jats:italic>\n            with all included datasets and evaluation procedures.\n          <\/jats:p>","DOI":"10.1145\/3485666","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T13:52:21Z","timestamp":1652795541000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Political Text Scaling Meets Computational Semantics"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2484-4331","authenticated-orcid":false,"given":"Federico","family":"Nanni","sequence":"first","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Mannheim, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1301-6314","authenticated-orcid":false,"given":"Goran","family":"Glava\u0161","sequence":"additional","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Mannheim, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9615-6389","authenticated-orcid":false,"given":"Ines","family":"Rehbein","sequence":"additional","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Mannheim, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7484-2049","authenticated-orcid":false,"given":"Simone Paolo","family":"Ponzetto","sequence":"additional","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Mannheim, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0209-3859","authenticated-orcid":false,"given":"Heiner","family":"Stuckenschmidt","sequence":"additional","affiliation":[{"name":"Data and Web Science Group, University of Mannheim, Mannheim, Germany"}]}],"member":"320","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"33","volume-title":"Proceedings of the 12th Conference of the European Chapter of the ACL (EACL\u201909)","author":"Agirre Eneko","year":"2009","unstructured":"Eneko Agirre and Aitor Soroa. 2009. Personalizing PageRank for word sense disambiguation. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL\u201909). Association for Computational Linguistics, 33\u201341. Retrieved from https:\/\/www.aclweb.org\/anthology\/E09-1005."},{"key":"e_1_3_2_3_2","unstructured":"Ryan Bakker Liesbet Hooghe Seth Jolly Gary Marks Jonathan Polk Jan Rovny Marco Steenbergen and Milada Anna Vachudova. 2020. 1999\u20132019 Chapel Hill Expert Survey Trend File. Version 1.2.Retrievedfrom:chesdata.eu."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"issue":"3","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1080\/19312458.2019.1594741","article-title":"Validating Wordscores: The promises and pitfalls of computational text scaling","volume":"13","author":"Bruinsma Bastiaan","year":"2019","unstructured":"Bastiaan Bruinsma and Kostas Gemenis. 2019. Validating Wordscores: The promises and pitfalls of computational text scaling. Commun. Methods Meas. 13, 3 (2019), 212\u2013227. DOI:https:\/\/doi.org\/10.1080\/19312458.2019.1594741","journal-title":"Commun. Methods Meas."},{"issue":"1","key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.electstud.2006.04.002","article-title":"Do they work? Validating computerised word frequency estimates against policy series","volume":"26","author":"Budge Ian","year":"2007","unstructured":"Ian Budge and Paul Pennings. 2007. Do they work? Validating computerised word frequency estimates against policy series. Elector. Stud. 26, 1 (2007), 121\u2013129.","journal-title":"Elector. Stud."},{"issue":"1","key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.electstud.2006.03.010","article-title":"Missing the message and shooting the messenger: Benoit and Laver\u2019s \u201cResponse.\u201d","volume":"26","author":"Budge Ian","year":"2007","unstructured":"Ian Budge and Paul Pennings. 2007. Missing the message and shooting the messenger: Benoit and Laver\u2019s \u201cResponse.\u201d Elector. Stud. 26, 1 (2007), 136\u2013141.","journal-title":"Elector. Stud."},{"key":"e_1_3_2_8_2","unstructured":"Matthew J. Denny and Arthur Spirling. 2016. Assessing the Consequences of Text Preprocessing Decisions. Retrieved from https:\/\/ssrn.com\/abstract=2849145."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1017\/pan.2017.44"},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"498","DOI":"10.4135\/9781526486387.n30","volume-title":"The SAGE Handbook of Research Methods in Political Science and International Relations. Volume 1","author":"Egerod Benjamin C. K.","year":"2020","unstructured":"Benjamin C. K. Egerod and Robert Klemmensen. 2020. Scaling political positions from text: Assumptions, Methods and Pitfalls. In The SAGE Handbook of Research Methods in Political Science and International Relations. Volume 1, Luigi Curini and Robert Franzese (Eds.). SAGE Publications, United Kingdom, 498\u2013521. DOI:https:\/\/doi.org\/10.4135\/9781526486387.n30"},{"key":"e_1_3_2_11_2","first-page":"498","volume-title":"Scaling Political Positions from Text: Assumptions, Methods and Pitfalls","author":"Egerod Benjamin C. K.","year":"2020","unstructured":"Benjamin C. K. Egerod and Robert Klemmensen. 2020. Scaling Political Positions from Text: Assumptions, Methods and Pitfalls. SAGE Publications, United Kingdom, 498\u2013521. DOI:https:\/\/doi.org\/10.4135\/9781526486387.n30"},{"key":"e_1_3_2_12_2","volume-title":"Studies in Linguistic Analysis","author":"Firth J.","year":"1957","unstructured":"J. Firth. 1957. A synopsis of linguistic theory 1930\u20131955. In Studies in Linguistic Analysis. Philological Society, Oxford."},{"key":"e_1_3_2_13_2","first-page":"2619","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Ganea Octavian-Eugen","year":"2017","unstructured":"Octavian-Eugen Ganea and Thomas Hofmann. 2017. Deep joint entity disambiguation with local neural attention. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2619\u20132629."},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/1467-9248.12015","article-title":"What to do (and Not to Do) with the Comparative Manifestos Project data","volume":"61","author":"Gemenis Kostas","year":"2013","unstructured":"Kostas Gemenis. 2013. What to do (and Not to Do) with the Comparative Manifestos Project data. Polit. Stud. 61 (2013), 3\u201323.","journal-title":"Polit. Stud."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.11.041"},{"key":"e_1_3_2_16_2","first-page":"688","volume-title":"Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics","author":"Glava\u0161 Goran","year":"2017","unstructured":"Goran Glava\u0161, Federico Nanni, and Simone Paolo Ponzetto. 2017. Unsupervised cross-lingual scaling of political texts. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. 688\u2013693."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1185"},{"key":"e_1_3_2_18_2","first-page":"181","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics","author":"Glava\u0161 Goran","year":"2018","unstructured":"Goran Glava\u0161 and Ivan Vuli\u0107. 2018. Discriminating between lexico-semantic relations with the specialization tensor model. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. 181\u2013187."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_2_20_2","article-title":"The nuts and bolts of automated text analysis. Comparing different document pre-processing techniques in four countries","author":"Greene Zac","year":"2016","unstructured":"Zac Greene, Andrea Ceron, Gijs Schumacher, and Zoltan Fazekas. 2016. The nuts and bolts of automated text analysis. Comparing different document pre-processing techniques in four countries. Work. Paper (Nov. 2016). DOI:https:\/\/doi.org\/10.31219\/osf.io\/ghxj8","journal-title":"Work. Paper"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mps028"},{"key":"e_1_3_2_22_2","first-page":"74","volume-title":"Proceedings of the International Conference on the Frontiers and Advances in Data Science (FADS)","author":"Gurciullo Stefano","year":"2017","unstructured":"Stefano Gurciullo and Slava J. Mikhaylov. 2017. Detecting policy preferences and dynamics in the un general debate with neural word embeddings. In Proceedings of the International Conference on the Frontiers and Advances in Data Science (FADS). IEEE, 74\u201379."},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the EPSA Annual Conference","author":"Hawkins Kirk A.","year":"2019","unstructured":"Kirk A. Hawkins, Rosario Aguilar, Bruno Castanho Silva, Erik K. Jenne, Bojana Kocijan, and Crist\u00f3bal Rovira Kaltwasser. 2019. Measuring populist discourse: The global populism database. In Proceedings of the EPSA Annual Conference."},{"issue":"2","key":"e_1_3_2_24_2","article-title":"Computers, coders, and voters: Comparing automated methods for estimating party positions","volume":"2","author":"Hjorth Frederik","year":"2015","unstructured":"Frederik Hjorth, Robert Klemmensen, Sara Hobolt, Martin Ejnar Hansen, and Peter Kurrild-Klitgaard. 2015. Computers, coders, and voters: Comparing automated methods for estimating party positions. Res. Polit. 2, 2 (6 2015). DOI:https:\/\/doi.org\/10.1177\/2053168015580476","journal-title":"Res. Polit."},{"key":"e_1_3_2_25_2","volume-title":"Proceedings of the 6th Workshop on Automated Knowledge Base Construction (AKBC)","author":"Joulin Armand","year":"2017","unstructured":"Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, and Tomas Mikolov. 2017. Fast linear model for knowledge graph embeddings. In Proceedings of the 6th Workshop on Automated Knowledge Base Construction (AKBC)."},{"key":"e_1_3_2_26_2","first-page":"169","article-title":"Coder training: Key to enhancing reliability and validity","volume":"3","author":"Lacewell Onawa P.","year":"2013","unstructured":"Onawa P. Lacewell and Annika Werner. 2013. Coder training: Key to enhancing reliability and validity. Map. Polic. Pref. Texts 3 (2013), 169\u2013194.","journal-title":"Map. Polic. Pref. Texts"},{"key":"e_1_3_2_27_2","first-page":"282","volume-title":"Proceedings of the 18th International Conference on Machine Learning","author":"Lafferty John D.","year":"2001","unstructured":"John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning. 282\u2013289."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1030"},{"issue":"2","key":"e_1_3_2_29_2","first-page":"311","article-title":"Extracting policy positions from political texts using words as data","volume":"97","author":"Laver Michael","year":"2018","unstructured":"Michael Laver and Kenneth Benoit. 2018. Extracting policy positions from political texts using words as data. Amer. Polit. Sci. Rev. 97, 2 (2018), 311\u2013331. DOI:https:\/\/doi.org\/10.1017\/S0003055403000698","journal-title":"Amer. Polit. Sci. Rev."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0003055403000698"},{"key":"e_1_3_2_31_2","first-page":"1188","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning. 1188\u20131196."},{"issue":"16","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1093\/pan\/mpn004","article-title":"Understanding wordscores","volume":"4","author":"Lowe Will","year":"2008","unstructured":"Will Lowe. 2008. Understanding wordscores. Polit. Anal. 4, 16 (2008), 356\u2013371.","journal-title":"Polit. Anal."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpt002"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-9162.2010.00006.x"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1101"},{"key":"e_1_3_2_36_2","first-page":"55","volume-title":"Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics","author":"Manning Christopher","year":"2014","unstructured":"Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics. 55\u201360."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.5555\/311445"},{"issue":"10","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1093\/pan\/10.2.134","article-title":"Dynamic ideal point estimation via Markov chain Monte Carlo for the US Supreme Court, 1953\u20131999","volume":"2","author":"Martin Andrew","year":"2002","unstructured":"Andrew Martin and Kevin Quinn. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the US Supreme Court, 1953\u20131999. Polit. Anal. 2, 10 (2002), 134\u2013153.","journal-title":"Polit. Anal."},{"issue":"16","key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1093\/pan\/mpm010","article-title":"A robust transformation procedure for interpreting political text","volume":"1","author":"Martin Lanny","year":"2008","unstructured":"Lanny Martin and Georg Vanberg. 2008. A robust transformation procedure for interpreting political text. Polit. Anal. 1, 16 (2008), 93\u2013100.","journal-title":"Polit. Anal."},{"issue":"2","key":"e_1_3_2_40_2","first-page":"1","article-title":"The manifesto corpus: A new resource for research on political parties and quantitative text analysis","volume":"3","author":"Merz Nicolas","year":"2016","unstructured":"Nicolas Merz, Sven Regel, and Jirka Lewandowski. 2016. The manifesto corpus: A new resource for research on political parties and quantitative text analysis. Res. Polit. 3, 2 (2016), 1\u20138. DOI:https:\/\/doi.org\/10.1177\/2053168016643346","journal-title":"Res. Polit."},{"issue":"1","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1093\/pan\/mpr047","article-title":"Coder reliability and misclassification in the human coding of party manifestos","volume":"20","author":"Mikhaylov Slava","year":"2012","unstructured":"Slava Mikhaylov, Michael Laver, and Kenneth R. Benoit. 2012. Coder reliability and misclassification in the human coding of party manifestos. Polit. Anal. 20, 1 (2012), 78\u201391. DOI:https:\/\/doi.org\/10.1093\/pan\/mpr047","journal-title":"Polit. Anal."},{"key":"e_1_3_2_42_2","first-page":"3111","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3111\u20133119."},{"key":"e_1_3_2_43_2","first-page":"59","volume-title":"Proceedings of the LREC Workshop ParlaCLARIN","author":"Nanni Federico","year":"2018","unstructured":"Federico Nanni, Goran Glava\u0161, Simone Paolo Ponzetto, Sara Tonelli, Nicol\u00f2 Conti, Ahmet Aker, Alessio Palmero Aprosio, Arnim Bleier, Benedetta Carlotti, Theresa Gessler, Tim Henrichsen, Dirk Hovy, Christian Kahmann, Mladen Karan, Akitaka Matsuo, Stefano Menini, Dong Nguyen, Andreas Niekler, Lisa Posch, Federico Vegetti, Zeerak Waseem, Tanya Whyte, and Nikoleta Yordanova. 2018. Findings from the hackathon on understanding Euroscepticism through the lens of textual data. In Proceedings of the LREC Workshop ParlaCLARIN. 59\u201366."},{"key":"e_1_3_2_44_2","unstructured":"Federico Nanni Goran Glava\u0161 Simone Paolo Ponzetto and Heiner Stuckenschmidt. 2019. Online Appendix: Political Text Scaling Meets Computational Semantics. Retrieved from https:\/\/federiconanni.com\/semantic-scaling\/."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2483592"},{"issue":"6","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1177\/1354068820927686","article-title":"Measuring populism worldwide","volume":"26","author":"Norris Pippa","year":"2020","unstructured":"Pippa Norris. 2020. Measuring populism worldwide. Party Polit. 26, 6 (2020), 697\u2013717. DOI:https:\/\/doi.org\/10.1177\/1354068820927686","journal-title":"Party Polit."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_48_2","unstructured":"Patrick O. Perry and Kenneth Benoit. 2017. Scaling Text with the Class Affinity Model. Retrieved from https:\/\/arxiv.org\/abs\/1710.08963."},{"key":"e_1_3_2_49_2","first-page":"238","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Pershina Maria","year":"2015","unstructured":"Maria Pershina, Yifan He, and Ralph Grishman. 2015. Personalized page rank for named entity disambiguation. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 238\u2013243. DOI:https:\/\/doi.org\/10.3115\/v1\/N15-1026"},{"issue":"1","key":"e_1_3_2_50_2","article-title":"Explaining the salience of anti-elitism and reducing political corruption for political parties in Europe with the 2014 Chapel Hill Expert Survey data","volume":"4","author":"Polk Jonathan","year":"2017","unstructured":"Jonathan Polk, Jan Rovny, Ryan Bakker, Erica Edwards, Liesbet Hooghe, Seth Jolly, Jelle Koedam, Filip Kostelka, Gary Marks, Gijs Schumacher, Marco Steenbergen, Milada Vachudova, Marko Zilovic, and Polk Jonathan. 2017. Explaining the salience of anti-elitism and reducing political corruption for political parties in Europe with the 2014 Chapel Hill Expert Survey data. Res. Polit. 4, 1 (1 2017). DOI:https:\/\/doi.org\/10.1177\/2053168016686915","journal-title":"Res. Polit."},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.2307\/2111172"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1108\/eb046814"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1017\/S0007123409990299","article-title":"Position taking in european parliament speeches","volume":"52","author":"Proksch Sven-Oliver","year":"2010","unstructured":"Sven-Oliver Proksch and Jonathan B. Slapin. 2010. Position taking in european parliament speeches. Brit. J. Polit. Sci. 52 (2010), 587\u2013611.","journal-title":"Brit. J. Polit. Sci."},{"issue":"28","key":"e_1_3_2_54_2","first-page":"112","article-title":"Word embeddings for the analysis of ideological placement in parliamentary corpora","volume":"1","author":"Rheault Ludovic","year":"2019","unstructured":"Ludovic Rheault and Christopher Cochrane. 2019. Word embeddings for the analysis of ideological placement in parliamentary corpora. Polit. Anal. 1, 28 (2019), 112\u2013133.","journal-title":"Polit. Anal."},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","unstructured":"Pedro Rodriguez and Arthur Spirling. Forthcoming. Word embeddings: What works what doesn\u2019t and how to tell the difference for applied research. J. Polit. DOI:https:\/\/doi.org\/10.1086\/715162","DOI":"10.1086\/715162"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/0306-4573(88)90021-0"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-5907.2008.00338.x"},{"key":"e_1_3_2_58_2","first-page":"1117","volume-title":"Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers","author":"Wachsmuth Henning","year":"2017","unstructured":"Henning Wachsmuth, Benno Stein, and Yamen Ajjour. 2017. \u201cPageRank\u201d for argument relevance. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, 1117\u20131127. Retrieved from https:\/\/www.aclweb.org\/anthology\/E17-1105."},{"key":"e_1_3_2_59_2","first-page":"412","volume-title":"Proceedings of the 14th International Conference on Machine Learning","author":"Yang Yiming","year":"1997","unstructured":"Yiming Yang and Jan O. Pedersen. 1997. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning. 412\u2013420."},{"key":"e_1_3_2_60_2","first-page":"912","volume-title":"Proceedings of the 20th International Conference on Machine Learning","author":"Zhu Xiaojin","year":"2003","unstructured":"Xiaojin Zhu, Zoubin Ghahramani, and John D. Lafferty. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning. 912\u2013919."}],"container-title":["ACM\/IMS Transactions on Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485666","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485666","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:40Z","timestamp":1750191520000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,11,30]]}},"alternative-id":["10.1145\/3485666"],"URL":"https:\/\/doi.org\/10.1145\/3485666","relation":{},"ISSN":["2691-1922"],"issn-type":[{"type":"print","value":"2691-1922"}],"subject":[],"published":{"date-parts":[[2021,11,30]]},"assertion":[{"value":"2020-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}