Abstract
Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to approach entity linking in queries, with a special focus on how to strike a balance between effectiveness and efficiency. We divide the task of entity linking in queries to two main steps: candidate entity ranking and disambiguation, and explore both unsupervised and supervised alternatives for each step. Our main finding is that best overall performance (in terms of efficiency and effectiveness) can be achieved by employing supervised learning for the entity ranking step, while tackling disambiguation with a simple unsupervised algorithm. Using the Entity Recognition and Disambiguation Challenge platform, we further demonstrate that our recommended method achieves state-of-the-art performance.
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Notes
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It is important to note that Y-ERD contains queries that have been reformulated (often only slightly so) during the course of a search session; we ensure that queries from the same session are assigned to the same fold when using cross-validation.
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References
Blanco, R., Ottaviano, G., Meij, E.: Fast and space-efficient entity linking in queries. In: Proceedings of WSDM, pp. 179–188 (2015)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Carmel, D., Chang, M.-W., Gabrilovich, E., Hsu, B.-J.P., Wang, K.: ERD 2014: entity recognition and disambiguation challenge. In: ACM SIGIR Forum, vol. 48, pp. 63–77 (2014)
Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., Trani, S.: Learning relatedness measures for entity linking. In: Proceedings of CIKM, pp. 139–148 (2013)
Chiu, Y.-P., Shih, Y.-S., Lee, Y.-Y., Shao, C.-C., Cai, M.-L., Wei, S.-L., Chen, H.-H.: NTUNLP approaches to recognizing and disambiguating entities in long and short text at the ERD challenge 2014. In: Proceedings of ERD@SIGIR (2014)
Cornolti, M., Ferragina, P., Ciaramita, M., Rüd, S., Schütze, H.: A piggyback system for joint entity mention detection and linking in web queries. In: Proceedings of WWW, pp. 567–578 (2016)
Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of EMNLP-CoNLL, pp. 708–716 (2007)
Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: Proceedings of SIGIR, pp. 365–374 (2014)
Deepak, P., Ranu, S., Banerjee, P., Mehta, S.: Entity linking for web search queries. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 394–399. Springer, Cham (2015). doi:10.1007/978-3-319-16354-3_43
Eckhardt, A., Hreško, J., Procházka, J., Smrs, O.: Entity linking based on the co-occurrence graph and entity probability. In: Proceeding of ERD@SIGIR (2014)
Ferragina, P., Scaiella, U.: TAGME: on-the-fly annotation of short text fragments (by Wikipedia entities). In: Proceedings of CIKM, pp. 1625–1628 (2010)
Gabrilovich, E., Ringgaard, M., Subramanya, A.: FACC1: freebase annotation of ClueWeb corpora, Version 1 (2013)
Guo, S., Chang, M.-W., Kiciman, E.: To link or not to link? A study on end-to-end tweet entity linking. In: HLT-NAACL, pp. 1020–1030 (2013)
Hachey, B., Radford, W., Nothman, J., Honnibal, M., Curran, J.R.: Evaluating entity linking with Wikipedia. Artif. Intell. 194, 130–150 (2013)
Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of SIGIR, pp. 765–774 (2011)
Hasibi, F., Balog, K., Bratsberg, S.E.: Entity linking in queries: tasks and evaluation. In: Proceedings of ICTIR, pp. 171–180 (2015)
Hasibi, F., Balog, K., Bratsberg, S.E.: Exploiting entity linking in queries for entity retrieval. In: Proceedings of ICTIR, pp. 209–218 (2016)
Hasibi, F., Balog, K., Bratsberg, S.E.: On the reproducibility of the TAGME entity linking system. In: Ferro, N., Crestani, F., Moens, M.-F., Mothe, J., Silvestri, F., Nunzio, G.M., Hauff, C., Silvello, G. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 436–449. Springer, Cham (2016). doi:10.1007/978-3-319-30671-1_32
Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust disambiguation of named entities in text. In: Proceedings of EMNLP, pp. 782–792 (2011)
Kraaij, W., Spitters, M.: Language models for topic tracking. In: Language Modeling for Information Retrieval, pp. 95–123 (2003)
Kulkarni, S., Singh, A., Ramakrishnan, G., Chakrabarti, S.: Collective annotation of Wikipedia entities in web text. In: Proceedings of SIGKDD, pp. 457–466 (2009)
Medelyan, O., Witten, I.H., Milne, D.: Topic indexing with Wikipedia. In: Proceedings of the Wikipedia and AI Workshop at the AAAI 2008 Conference (2008)
Meij, E., Weerkamp, W., de Rijke, M.: Adding semantics to microblog posts. In: Proceedings of WSDM, pp. 563–572 (2012)
Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: Proceedings of CIKM, pp. 233–242 (2007)
Milne, D., Witten, I.H.: Learning to link with Wikipedia. In: Proceedings of CIKM, pp. 509–518 (2008)
Neumayer, R., Balog, K., Nørvåg, K.: When simple is (more than) good enough: effective semantic search with (almost) no semantics. In: Baeza-Yates, R., Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 540–543. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28997-2_59
Ogilvie, P., Callan, J.: Combining document representations for known-item search. In: Proceedings of SIGIR, pp. 143–150 (2003)
Schuhmacher, M., Dietz, L., Paolo Ponzetto, S.: Ranking entities for Web queries through text and knowledge. In: Proceedings of CIKM, pp. 1461–1470 (2015)
Sen, P.: Collective context-aware topic models for entity disambiguation. In: Proceedings of WWW, pp. 729–738 (2012)
Usbeck, R. et al.: GERBIL: general entity annotator benchmarking framework. In: Proceedings of WWW, pp. 1133–1143 (2015)
Xiong, C., Callan, J.: EsdRank: Connecting query and documents through external semi-structured data. In: Proceedings of CIKM, pp. 951–960 (2015)
Yilmaz, E., Verma, M., Mehrotra, R., Kanoulas, E., Carterette, B., Craswell, N.: Overview of the TREC 2015 tasks track. In: Proceedings of TREC (2015)
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Hasibi, F., Balog, K., Bratsberg, S.E. (2017). Entity Linking in Queries: Efficiency vs. Effectiveness. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_4
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