@inproceedings{Lenz2022ComparingUnsupervisedAlgorithms,
	title = {Comparing {{Unsupervised Algorithms}} to {{Construct Argument Graphs}}},
	booktitle = {Joint {{Proceedings}} of {{Workshops}}, {{Tutorials}} and {{Doctoral Consortium}} Co-Located with the 45rd {{German Conference}} on {{Artificial Intelligence}}},
	author = {Lenz, Mirko and Dumani, Lorik and Sahitaj, Premtim},
	editor = {Koert, Dorothea and Minor, Mirjam},
	year = {2022},
	month = sep,
	series = {{{CEUR Workshop Proceedings}}},
	volume = {3457},
	publisher = {CEUR},
	address = {Virtual Event, Trier},
	issn = {1613-0073},
	url        = {http://www.wi2.uni-trier.de/shared/publications/Lenz2022ComparingUnsupervisedAlgorithms.pdf},
	abstract   = {Computational argumentation has gained considerable attention in recent years. Various areas have been addressed, such as extracting arguments from natural language texts into a structured form in order to store them in an argument base, determining stances for arguments with respect to topics, determination of inferences from statements, and much more. After so much progress has been made in the isolated tasks, in this paper we address the next level and aim to advance the automatic generation of argument graphs. To this end, we investigate various unsupervised methods for constructing the graphs and measure the performance with different metrics on three different datasets. Our implementation is publicly available on GitHub under the permissive MIT license.},
}

{"_id":"i7d6Ceabuorjp7vpq","bibbaseid":"lenz-dumani-sahitaj-comparingunsupervisedalgorithmstoconstructargumentgraphs-2022","author_short":["Lenz, M.","Dumani, L.","Sahitaj, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Comparing Unsupervised Algorithms to Construct Argument Graphs","booktitle":"Joint Proceedings of Workshops, Tutorials and Doctoral Consortium Co-Located with the 45rd German Conference on Artificial Intelligence","author":[{"propositions":[],"lastnames":["Lenz"],"firstnames":["Mirko"],"suffixes":[]},{"propositions":[],"lastnames":["Dumani"],"firstnames":["Lorik"],"suffixes":[]},{"propositions":[],"lastnames":["Sahitaj"],"firstnames":["Premtim"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Koert"],"firstnames":["Dorothea"],"suffixes":[]},{"propositions":[],"lastnames":["Minor"],"firstnames":["Mirjam"],"suffixes":[]}],"year":"2022","month":"September","series":"CEUR Workshop Proceedings","volume":"3457","publisher":"CEUR","address":"Virtual Event, Trier","issn":"1613-0073","url":"http://www.wi2.uni-trier.de/shared/publications/Lenz2022ComparingUnsupervisedAlgorithms.pdf","abstract":"Computational argumentation has gained considerable attention in recent years. Various areas have been addressed, such as extracting arguments from natural language texts into a structured form in order to store them in an argument base, determining stances for arguments with respect to topics, determination of inferences from statements, and much more. After so much progress has been made in the isolated tasks, in this paper we address the next level and aim to advance the automatic generation of argument graphs. To this end, we investigate various unsupervised methods for constructing the graphs and measure the performance with different metrics on three different datasets. Our implementation is publicly available on GitHub under the permissive MIT license.","bibtex":"@inproceedings{Lenz2022ComparingUnsupervisedAlgorithms,\n\ttitle = {Comparing {{Unsupervised Algorithms}} to {{Construct Argument Graphs}}},\n\tbooktitle = {Joint {{Proceedings}} of {{Workshops}}, {{Tutorials}} and {{Doctoral Consortium}} Co-Located with the 45rd {{German Conference}} on {{Artificial Intelligence}}},\n\tauthor = {Lenz, Mirko and Dumani, Lorik and Sahitaj, Premtim},\n\teditor = {Koert, Dorothea and Minor, Mirjam},\n\tyear = {2022},\n\tmonth = sep,\n\tseries = {{{CEUR Workshop Proceedings}}},\n\tvolume = {3457},\n\tpublisher = {CEUR},\n\taddress = {Virtual Event, Trier},\n\tissn = {1613-0073},\n\turl = {http://www.wi2.uni-trier.de/shared/publications/Lenz2022ComparingUnsupervisedAlgorithms.pdf},\n\tabstract = {Computational argumentation has gained considerable attention in recent years. Various areas have been addressed, such as extracting arguments from natural language texts into a structured form in order to store them in an argument base, determining stances for arguments with respect to topics, determination of inferences from statements, and much more. After so much progress has been made in the isolated tasks, in this paper we address the next level and aim to advance the automatic generation of argument graphs. To this end, we investigate various unsupervised methods for constructing the graphs and measure the performance with different metrics on three different datasets. Our implementation is publicly available on GitHub under the permissive MIT license.},\n}\n\n","author_short":["Lenz, M.","Dumani, L.","Sahitaj, P."],"editor_short":["Koert, D.","Minor, M."],"key":"Lenz2022ComparingUnsupervisedAlgorithms","id":"Lenz2022ComparingUnsupervisedAlgorithms","bibbaseid":"lenz-dumani-sahitaj-comparingunsupervisedalgorithmstoconstructargumentgraphs-2022","role":"author","urls":{"Paper":"http://www.wi2.uni-trier.de/shared/publications/Lenz2022ComparingUnsupervisedAlgorithms.pdf"},"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"https://web.wi2.uni-trier.de/publications/WI2Publikationen.bib","dataSources":["MSp3DzP4ToPojqkFy"],"keywords":[],"search_terms":["comparing","unsupervised","algorithms","construct","argument","graphs","lenz","dumani","sahitaj"],"title":"Comparing Unsupervised Algorithms to Construct Argument Graphs","year":2022}