Enriching temporal query understanding through date identification: how to tag implicit temporal queries?

ABSTRACT

Generically, search engines fail to understand the user's temporal intents when expressed as implicit temporal queries. This causes the retrieval of less relevant information and prevents users from being aware of the possible temporal dimension of the query results. In this paper, we aim to develop a language-independent model that tackles the temporal dimensions of a query and identifies its most relevant time periods. For this purpose, we propose a temporal similarity measure capable of associating a relevant date(s) to a given query and filtering out irrelevant ones. Our approach is based on the exploitation of temporal information from web content, particularly within the set of k-top retrieved web snippets returned in response to a query. We particularly focus on extracting years, which are a kind of temporal information that often appears in this type of collection. We evaluate our methodology using a set of real-world text temporal queries, which are clear concepts (i.e. queries which are non-ambiguous in concept and temporal in their purpose). Experiments show that when compared to baseline methods, determining the most relevant dates relating to any given implicit temporal query can be improved with a new temporal similarity measure.

References

1. ANNIE (2002). http://www.aktors.org/technologies/annie/
2. Alonso, O., Baeza-Yates, R., and Gertz, M. (2009). Effectiveness of Temporal Snippets. In WSSP'09 - WWW'09. Madrid, Spain.
3. Alonso, O., Baeza-Yates, R., & Gertz, M. (2007). Exploratory Search Using Timelines. In ESCHI - CHI'07. San Jose, USA.
4. Alonso, O., Gertz, M., and Baeza-Yates, R. (2009). Clustering and Exploring Search Results using Timeline Constructions. In CIKM'09.
5. Alonso, O., Gertz, M., and Baeza-Yates, R. (2011). Enhancing Document Snippets Using Temp. Information. LNCS 7024, 26--31.
6. Berberich, K., Bedathur, S., Alonso, O., and Weikum, G. (2010). A Language Modeling Approach for Temporal Information Needs. LNCS, 5993, 13--25.
7. Bollegala, D., Matsuo, Y., and Ishizuka, M. (2007). Measuring Semantic Similarity between Words Using Web Search Engines. In WWW'07, 757--766. Banff, Canada. May 8--12.
8. Campos, R. (2011). http://www.ccc.ipt.pt/~ricardo/software
9. Campos, R., Dias, G., and Jorge, A. M. (2011). What is the Temporal Value of Web Snippets? In WWW'11-TWAW, Hyderabad, India.
10. Campos, R., Jorge, A., & Dias, G. (2011). Using Web Snippets and Query-logs to Measure Implicit Temporal Intents in Queries. In SIGIR'11-QRU, 13--16. Beijing, China. July 28.
11. Church, K., and Hanks, P. (1990). Word Association Norms Mutual Information and Lexicography. In Comp. Linguistics, 16(1), 23--29.
12. Cilibrasi, R. L., and Vitányi, P. M. (2007). The Google Similarity Distance. In IEEE TKDE, 19(3), 370--373
13. Dakka, W., Gravano, L., and Ipeirotis, P. G. (2008). Answering General Time Sensitive Queries. In CIKM'08, 1437--1438.
14. Deerwester, S., Dumais, S., Landauer, T., Furnas, G., and Harshman, R. (1990). Indexing by Latent Semantic Analysis. In Journal of the American Society for Information Science, 41(6), 391--407.
15. Dias, G., Alves, E., and Lopes, J. (2007). Topic Segmentation Algorithms for Text Summarization and Passage Retrieval: An Exhaustive Evaluation. In AAAI'07, 1334--1340. Canada. July 22--26.
16. Dice, L. R. (1945). Measures of the Amount of Ecologic Association between Species. In Ecological Society of America, 26, 297--302.
17. Dumais, S. T. (2005). Latent Semantic Analysis. In Annual Review of Information Science and Technology, 38(1), 188--230.
18. Freitag, D., Blume, M., Byrnes, J., Chow, E., Kapadia, S., Rohwer, R., et al. (2005). New Experiments in Distributional Representations of Synonymy. In CoNLL'05, 25--32. Michigan, USA.
19. Georgetown University. (2002). GUTime Download. http://www.timeml.org/site/tarsqi/modules/gutime/download.html
20. Google Insights (2011). http://www.google.com/insights/search
21. Ikehara, S., Murakami, J., and Kimoto, Y. (2003). Vector Space Model based on Semantic Attributes of Words. In JNLP: Journal of Natural Language Processing, 10(2), 111--128.
22. Jaccard, P. (1901). Étude comparative de la distribution florale dans une portion des Alpes et des Jura. In Bulletin del la Société Vaudoise des Sciences Naturelles, 37, 547--579.
23. Jones, R., and Diaz, F. (2007). Temporal Profiles of Queries. In TOIS: ACM Transactions on Information Systems, 25(3).
24. Kanhabua, N., and Nørvåg, K. (2010). Determining Time of Queries for Re-Ranking Search Results. In ECDL'10. Glasgow, Scotland.
25. Katzell, R. A., and Cureton, E. E. (1947). Biserial Correlation and Prediction. In The Journal of Psychology, 24(2), 273--278.
26. Machado, D., Barbosa, T., Pais, S., Martins, B., and Dias, G. Universal Mobile Information Retrieval. In HCII'09, 345--354. USA.
27. McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. In Psychometrika 12(2), 153--157.
28. Metzler, D., Jones, R., Peng, F., and Zhang, R. (2009). Improving Search Relevance for Implicitly Temporal Queries. In SIGIR'09, 700--701. Boston, USA. July 19--23.
29. Rogers, D. J., and Tanimoto, T. T. (1960). A Computer Program for Classifying Plants. In Science, 132, 1115--1118.
30. Ruprecht-Karl University Heidelberg. (2011). Temporal Tagging. http://dbs.ifi.uni-heidelberg.de/index.php?id=129#c784
31. Silva, J. F., Dias, G., Guilloré, S., and Pereira, J. G. (1999). Using LocalMaxs Algorithm for the Extraction of Contiguous and Non-contiguous Multiword Lexical Units. In EPIA'99, 21--24. Portugal.
32. Turney, P. D. (2001). Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In EMCL'01, 491--502. Freiburg, Germany.

Index Terms

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

    • Information retrieval query processing