Details
| Original language | English |
|---|---|
| Title of host publication | CHIIR 2016 - Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval |
| Pages | 183-192 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450337519 |
| Publication status | Published - 2016 |
| Event | CHIIR 2016: ACM SIGIR Conference on Human Information Interaction and Retrieval - Carrboro, United States Duration: 13 Mar 2016 → 17 Mar 2016 |
Abstract
Longitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for historians strongly influences their search behaviour in these archives. While searching for articles published over time, a key preference is to retrieve documents which cover the important aspects from important points in time which is different from standard search behavior. To support this search strategy, we introduce the notion of a Historical Query Intent to explicitly model a historian's search task and define an aspect-time diversification problem over news archives. We present a novel algorithm, HistDiv, that explicitly models the aspects and important time windows based on a historian's information seeking behavior. By incorporating temporal priors based on publication times and temporal expressions, we diversify both on the aspect and temporal dimensions. We test our methods by constructing a test collection based on The New York Times Collection with a workload of 30 queries of historical intent assessed manually. We find that HistDiv outperforms all competitors in subtopic recall with a slight loss in precision. We also present results of a qualitative user study to determine wether this drop in precision is detrimental to user experience. Our results show that users still preferred HistDiv's ranking.
ASJC Scopus subject areas
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Information Systems
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CHIIR 2016 - Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval. 2016. p. 183-192.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - History by Diversity
T2 - CHIIR 2016
AU - Singh, Jaspreet
AU - Nejdl, Wolfgang
AU - Anand, Avishek
N1 - Funding information: This work was carried out under the context of the ERC Grant (339233) ALEXANDRIA. We thank Prof. Jane Winters and her colleagues from the Institute of Historical Research at the University College of London for their help and cooperation.
PY - 2016
Y1 - 2016
N2 - Longitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for historians strongly influences their search behaviour in these archives. While searching for articles published over time, a key preference is to retrieve documents which cover the important aspects from important points in time which is different from standard search behavior. To support this search strategy, we introduce the notion of a Historical Query Intent to explicitly model a historian's search task and define an aspect-time diversification problem over news archives. We present a novel algorithm, HistDiv, that explicitly models the aspects and important time windows based on a historian's information seeking behavior. By incorporating temporal priors based on publication times and temporal expressions, we diversify both on the aspect and temporal dimensions. We test our methods by constructing a test collection based on The New York Times Collection with a workload of 30 queries of historical intent assessed manually. We find that HistDiv outperforms all competitors in subtopic recall with a slight loss in precision. We also present results of a qualitative user study to determine wether this drop in precision is detrimental to user experience. Our results show that users still preferred HistDiv's ranking.
AB - Longitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for historians strongly influences their search behaviour in these archives. While searching for articles published over time, a key preference is to retrieve documents which cover the important aspects from important points in time which is different from standard search behavior. To support this search strategy, we introduce the notion of a Historical Query Intent to explicitly model a historian's search task and define an aspect-time diversification problem over news archives. We present a novel algorithm, HistDiv, that explicitly models the aspects and important time windows based on a historian's information seeking behavior. By incorporating temporal priors based on publication times and temporal expressions, we diversify both on the aspect and temporal dimensions. We test our methods by constructing a test collection based on The New York Times Collection with a workload of 30 queries of historical intent assessed manually. We find that HistDiv outperforms all competitors in subtopic recall with a slight loss in precision. We also present results of a qualitative user study to determine wether this drop in precision is detrimental to user experience. Our results show that users still preferred HistDiv's ranking.
UR - http://www.scopus.com/inward/record.url?scp=84974602996&partnerID=8YFLogxK
U2 - 10.1145/2854946.2854959
DO - 10.1145/2854946.2854959
M3 - Conference contribution
AN - SCOPUS:84974602996
SP - 183
EP - 192
BT - CHIIR 2016 - Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval
Y2 - 13 March 2016 through 17 March 2016
ER -