Details
| Original language | English |
|---|---|
| Title of host publication | KI 2019: Advances in Artificial Intelligence |
| Subtitle of host publication | 42nd German Conference on AI, Proceedings |
| Editors | Christoph Benzmüller, Heiner Stuckenschmidt |
| Place of Publication | Berlin Heidelberg New York |
| Publisher | Springer Verlag |
| Pages | 48-59 |
| Number of pages | 12 |
| ISBN (electronic) | 978-3-030-30179-8 |
| ISBN (print) | 9783030301781 |
| Publication status | Published - 1 Sept 2019 |
| Externally published | Yes |
| Event | 42nd German Conference on Artificial Intelligence, KI 2019 - Kassel, Germany Duration: 23 Sept 2019 → 26 Sept 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Publisher | Springer Verlag |
| Volume | 11793 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Argument search is the study of search engine technology that can retrieve arguments for potentially controversial topics or claims upon user request. The design of an argument search engine is tied to its underlying argument acquisition paradigm. More specifically, the employed paradigm controls the trade-off between retrieval precision and recall and thus determines basic search characteristics: Compiling an exhaustive argument corpus offline benefits precision at the expense of recall, whereas retrieving arguments from the web on-the-fly benefits recall at the expense of precision. This paper presents the new corpus of our argument search engine args.me, which follows the former paradigm. We freely provide the corpus to the community. With 387 606 arguments it is one of the largest argument resources available so far. In a qualitative analysis, we compare the args.me corpus acquisition paradigm to that of two other argument search engines, and we report first empirical insights into how people search with args.me.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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KI 2019: Advances in Artificial Intelligence: 42nd German Conference on AI, Proceedings. ed. / Christoph Benzmüller; Heiner Stuckenschmidt. Berlin Heidelberg New York: Springer Verlag, 2019. p. 48-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11793 LNAI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Data Acquisition for Argument Search
T2 - 42nd German Conference on Artificial Intelligence, KI 2019
AU - Ajjour, Yamen
AU - Wachsmuth, Henning
AU - Kiesel, Johannes
AU - Potthast, Martin
AU - Hagen, Matthias
AU - Stein, Benno
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Argument search is the study of search engine technology that can retrieve arguments for potentially controversial topics or claims upon user request. The design of an argument search engine is tied to its underlying argument acquisition paradigm. More specifically, the employed paradigm controls the trade-off between retrieval precision and recall and thus determines basic search characteristics: Compiling an exhaustive argument corpus offline benefits precision at the expense of recall, whereas retrieving arguments from the web on-the-fly benefits recall at the expense of precision. This paper presents the new corpus of our argument search engine args.me, which follows the former paradigm. We freely provide the corpus to the community. With 387 606 arguments it is one of the largest argument resources available so far. In a qualitative analysis, we compare the args.me corpus acquisition paradigm to that of two other argument search engines, and we report first empirical insights into how people search with args.me.
AB - Argument search is the study of search engine technology that can retrieve arguments for potentially controversial topics or claims upon user request. The design of an argument search engine is tied to its underlying argument acquisition paradigm. More specifically, the employed paradigm controls the trade-off between retrieval precision and recall and thus determines basic search characteristics: Compiling an exhaustive argument corpus offline benefits precision at the expense of recall, whereas retrieving arguments from the web on-the-fly benefits recall at the expense of precision. This paper presents the new corpus of our argument search engine args.me, which follows the former paradigm. We freely provide the corpus to the community. With 387 606 arguments it is one of the largest argument resources available so far. In a qualitative analysis, we compare the args.me corpus acquisition paradigm to that of two other argument search engines, and we report first empirical insights into how people search with args.me.
UR - http://www.scopus.com/inward/record.url?scp=85072850961&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30179-8_4
DO - 10.1007/978-3-030-30179-8_4
M3 - Conference contribution
AN - SCOPUS:85072850961
SN - 9783030301781
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 48
EP - 59
BT - KI 2019: Advances in Artificial Intelligence
A2 - Benzmüller, Christoph
A2 - Stuckenschmidt, Heiner
PB - Springer Verlag
CY - Berlin Heidelberg New York
Y2 - 23 September 2019 through 26 September 2019
ER -