Answering general time sensitive queries

ABSTRACT

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for "recency" queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We extensively evaluated our techniques using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examined several alternatives for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. Our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.

REFERENCES

• 1: Nick Craswell , Stephen Robertson , Hugo Zaragoza , Michael Taylor, Relevance weighting for query independent evidence, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076106]
• 2: Rosie Jones , Fernando Diaz, Temporal profiles of queries, ACM Transactions on Information Systems (TOIS), v.25 n.3, p.14-es, July 2007 [doi>10.1145/1247715.1247720]
• 3: Victor Lavrenko , W. Bruce Croft, Relevance based language models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.120-127, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383972]
• 4: Xiaoyan Li , W. Bruce Croft, Time-based language models, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA [doi>10.1145/956863.956951]
• 5: I. Mani, J. Pustejovsky, and R. Gaizauskas. The Language of Time: A Reader. Oxford University Press, 2005.
• 6: Kathleen R. McKeown , Regina Barzilay , David Evans , Vasileios Hatzivassiloglou , Judith L. Klavans , Ani Nenkova , Carl Sable , Barry Schiffman , Sergey Sigelman, Tracking and summarizing news on a daily basis with Columbia's Newsblaster, Proceedings of the second international conference on Human Language Technology Research, p.280-285, March 24-27, 2002, San Diego, California
• 7: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 8: S. E. Robertson, The probability ranking principle in IR, Readings in information retrieval, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1997
• 9: Fei Song , W. Bruce Croft, A general language model for information retrieval, Proceedings of the eighth international conference on Information and knowledge management, p.316-321, November 02-06, 1999, Kansas City, Missouri, United States [doi>10.1145/319950.320022]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  H. Information Systems
  H.m MISCELLANEOUS

General Terms:
Algorithms, Experimentation, Performance

Keywords:
time-sensitive search