Daxenberger, Johannes ; Schiller, Benjamin ; Stahlhut, Chris ; Kaiser, Erik ; Gurevych, Iryna (2024)
ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario.
In: Datenbank-Spektrum : Zeitschrift für Datenbanktechnologien und Information Retrieval, 2020, 20 (2)
doi: 10.26083/tuprints-00024014
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large range of topics and in multiple languages. Its main strength is its generalization to very different textual sources including web crawls, news data, or customer reviews. We validated the technology with a focus on supporting decisions in innovation management as well as customer feedback analysis. Along with its public argument search engine and API, ArgumenText has released multiple datasets for argument classification and clustering. This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering. It also lays out exemplary industry applications and remaining challenges.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Daxenberger, Johannes ; Schiller, Benjamin ; Stahlhut, Chris ; Kaiser, Erik ; Gurevych, Iryna |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario |
| Sprache: | Englisch |
| Publikationsjahr: | 26 April 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | Juli 2020 |
| Ort der Erstveröffentlichung: | Berlin ; Heidelberg |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Datenbank-Spektrum : Zeitschrift für Datenbanktechnologien und Information Retrieval |
| Jahrgang/Volume einer Zeitschrift: | 20 |
| (Heft-)Nummer: | 2 |
| DOI: | 10.26083/tuprints-00024014 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/24014 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large range of topics and in multiple languages. Its main strength is its generalization to very different textual sources including web crawls, news data, or customer reviews. We validated the technology with a focus on supporting decisions in innovation management as well as customer feedback analysis. Along with its public argument search engine and API, ArgumenText has released multiple datasets for argument classification and clustering. This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering. It also lays out exemplary industry applications and remaining challenges. |
| Freie Schlagworte: | Argument Mining, Argument Clustering |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-240149 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 26 Apr 2024 12:33 |
| Letzte Änderung: | 02 Mai 2024 11:49 |
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| Suche nach Titel in: | TUfind oder in Google |
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- ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario. (deposited 26 Apr 2024 12:33) [Gegenwärtig angezeigt]
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