Details
| Original language | English |
|---|---|
| Title of host publication | WSDM 2015 |
| Subtitle of host publication | Proceedings of the 8th ACM International Conference on Web Search and Data Mining |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 339-348 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450333177 |
| Publication status | Published - 2 Feb 2015 |
| Event | 8th ACM International Conference on Web Search and Data Mining, WSDM 2015 - Shanghai, China Duration: 31 Jan 2015 → 6 Feb 2015 |
Publication series
| Name | WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining |
|---|
Abstract
Fully understanding an older news article requires context knowledge from the time of article creation. Finding information about such context is a tedious and time-consuming task, which distracts the reader. Simple contextualization via Wikification is not sufficient here. The retrieved context information has to be time-aware, concise (not full Wikipages) and focused on the coherence of the article topic. In this paper, we present an approach for time-aware re-contextualization, which takes those requirements into account in order to improve reading experience. For this purpose, we propose (1) different query formulation methods for retrieving contextualization candidates and (2) ranking methods taking into account topical and temporal relevance as well as complementarity with respect to the original text. We evaluate our proposed approaches through extensive experiments using real-world datasets and ground-truth consisting of over 9,400 article/context pairs. To this end, our experimental results show that our approaches retrieve contextualization information for older articles from the New York Times Archive with high precision and outperform baselines significantly.
Keywords
- Complementarity, Interpretation, News, Temporal context, Time-aware re-contextualization, Wikipedia
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
Cite this
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WSDM 2015 : Proceedings of the 8th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery (ACM), 2015. p. 339-348 (WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Back to the past
T2 - 8th ACM International Conference on Web Search and Data Mining, WSDM 2015
AU - Tran, Nam Khanh
AU - Ceroni, Andrea
AU - Kanhabua, Nattiya
AU - Niederée, Claudia
PY - 2015/2/2
Y1 - 2015/2/2
N2 - Fully understanding an older news article requires context knowledge from the time of article creation. Finding information about such context is a tedious and time-consuming task, which distracts the reader. Simple contextualization via Wikification is not sufficient here. The retrieved context information has to be time-aware, concise (not full Wikipages) and focused on the coherence of the article topic. In this paper, we present an approach for time-aware re-contextualization, which takes those requirements into account in order to improve reading experience. For this purpose, we propose (1) different query formulation methods for retrieving contextualization candidates and (2) ranking methods taking into account topical and temporal relevance as well as complementarity with respect to the original text. We evaluate our proposed approaches through extensive experiments using real-world datasets and ground-truth consisting of over 9,400 article/context pairs. To this end, our experimental results show that our approaches retrieve contextualization information for older articles from the New York Times Archive with high precision and outperform baselines significantly.
AB - Fully understanding an older news article requires context knowledge from the time of article creation. Finding information about such context is a tedious and time-consuming task, which distracts the reader. Simple contextualization via Wikification is not sufficient here. The retrieved context information has to be time-aware, concise (not full Wikipages) and focused on the coherence of the article topic. In this paper, we present an approach for time-aware re-contextualization, which takes those requirements into account in order to improve reading experience. For this purpose, we propose (1) different query formulation methods for retrieving contextualization candidates and (2) ranking methods taking into account topical and temporal relevance as well as complementarity with respect to the original text. We evaluate our proposed approaches through extensive experiments using real-world datasets and ground-truth consisting of over 9,400 article/context pairs. To this end, our experimental results show that our approaches retrieve contextualization information for older articles from the New York Times Archive with high precision and outperform baselines significantly.
KW - Complementarity
KW - Interpretation
KW - News
KW - Temporal context
KW - Time-aware re-contextualization
KW - Wikipedia
UR - http://www.scopus.com/inward/record.url?scp=84928735296&partnerID=8YFLogxK
U2 - 10.1145/2684822.2685315
DO - 10.1145/2684822.2685315
M3 - Conference contribution
AN - SCOPUS:84928735296
T3 - WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining
SP - 339
EP - 348
BT - WSDM 2015
PB - Association for Computing Machinery (ACM)
Y2 - 31 January 2015 through 6 February 2015
ER -