Abstract
This paper presents the idea of systematically integrating relation triples derived from Open Information Extraction (OpenIE) with neural rankers in order to improve the performance of the ad-hoc retrieval task. This is motivated by two reasons: (1) to capture longer-range semantic associations between keywords in documents, which would not otherwise be immediately identifiable by neural rankers; and (2) identify closely mentioned yet semantically unrelated content in the document that could lead to a document being incorrectly considered to be relevant for the query. Through our extensive experiments on three widely used TREC collections, we show that our idea consistently leads to noticeable performance improvements for neural rankers on a range of metrics.
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References
Aliannejadi, M., Zamani, H., Crestani, F., Croft, W.B.: Asking clarifying questions in open-domain information-seeking conversations. In: Piwowarski, B., Chevalier, M., Gaussier, É., Maarek, Y., Nie, J., Scholer, F. (eds.) Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 475–484. ACM (2019). https://doi.org/10.1145/3331184.3331265
Berger, A.L., Lafferty, J.D.: Information retrieval as statistical translation. In: Gey, F.C., Hearst, M.A., Tong, R.M. (eds.) SIGIR 1999: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, 15–19 August 1999, pp. 222–229. ACM (1999). https://doi.org/10.1145/312624.312681
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a Siamese time delay neural network. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems 6 [7th NIPS Conference], Denver, Colorado, USA, pp. 737–744. Morgan Kaufmann (1993). http://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network
Dai, Z., Xiong, C., Callan, J., Liu, Z.: Convolutional neural networks for soft-matching n-grams in ad-hoc search. In: Chang, Y., Zhai, C., Liu, Y., Maarek, Y. (eds.) Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, 5–9 February 2018, pp. 126–134. ACM (2018). https://doi.org/10.1145/3159652.3159659
Dietz, L., Kotov, A., Meij, E.: Utilizing knowledge graphs for text-centric information retrieval. In: Collins-Thompson, K., Mei, Q., Davison, B.D., Liu, Y., Yilmaz, E. (eds.) The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, 08–12 July 2018, pp. 1387–1390. ACM (2018). https://doi.org/10.1145/3209978.3210187
Ensan, F., Bagheri, E.: Document retrieval model through semantic linking. In: de Rijke, M., Shokouhi, M., Tomkins, A., Zhang, M. (eds.) Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, UK, 6–10 February 2017, pp. 181–190. ACM (2017). https://doi.org/10.1145/3018661.3018692
Gashteovski, K., Yu, M., Kotnis, B., Lawrence, C., Niepert, M., Glavas, G.: BenchIE: a framework for multi-faceted fact-based open information extraction evaluation. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, 22–27 May 2022, pp. 4472–4490. Association for Computational Linguistics (2022). https://doi.org/10.18653/v1/2022.acl-long.307
Guo, J., Fan, Y., Ai, Q., Croft, W.B.: A deep relevance matching model for ad-hoc retrieval. In: Mukhopadhyay, S., et al. (eds.) Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, 24–28 October 2016, pp. 55–64. ACM (2016). https://doi.org/10.1145/2983323.2983769
Guo, J., Fan, Y., Ji, X., Cheng, X.: Matchzoo: A learning, practicing, and developing system for neural text matching. In: Piwowarski, B., Chevalier, M., Gaussier, É., Maarek, Y., Nie, J., Scholer, F. (eds.) Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 1297–1300. ACM (2019). https://doi.org/10.1145/3331184.3331403
Haddad, D., Ghosh, J.: Learning more from less: Towards strengthening weak supervision for ad-hoc retrieval. In: Piwowarski, B., Chevalier, M., Gaussier, É., Maarek, Y., Nie, J., Scholer, F. (eds.) Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 857–860. ACM (2019). https://doi.org/10.1145/3331184.3331272
Karimzadehgan, M., Zhai, C.: Estimation of statistical translation models based on mutual information for ad hoc information retrieval. In: Crestani, F., Marchand-Maillet, S., Chen, H., Efthimiadis, E.N., Savoy, J. (eds.) Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, 19–23 July 2010, pp. 323–330. ACM (2010). https://doi.org/10.1145/1835449.1835505
Kolluru, K., Mohammed, M., Mittal, S., Chakrabarti, S., Mausam: Alignment-augmented consistent translation for multilingual open information extraction. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, 22–27 May 2022, pp. 2502–2517. Association for Computational Linguistics (2022). https://doi.org/10.18653/v1/2022.acl-long.179
MacAvaney, S., Feldman, S., Goharian, N., Downey, D., Cohan, A.: ABNIRML: analyzing the behavior of neural IR models. Trans. Assoc. Comput. Linguistics 10, 224–239 (2022). https://doi.org/10.1162/tacl_a_00457
Mausam, Schmitz, M., Soderland, S., Bart, R., Etzioni, O.: Open language learning for information extraction. In: Tsujii, J., Henderson, J., Pasca, M. (eds.) Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, Jeju Island, Korea, 12–14 July 2012, pp. 523–534. ACL (2012), https://aclanthology.org/D12-1048/
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, a meeting of SIGDAT, a Special Interest Group of the ACL, Doha, Qatar, 25–29 October 2014, pp. 1532–1543. ACL (2014). https://doi.org/10.3115/v1/d14-1162
Reinanda, R., Meij, E., de Rijke, M.: Knowledge graphs: an information retrieval perspective. Found. Trends Inf. Retr. 14(4), 289–444 (2020). https://doi.org/10.1561/1500000063
Shehata, D., Arabzadeh, N., Clarke, C.L.A.: Early stage sparse retrieval with entity linking. In: Hasan, M.A., Xiong, L. (eds.) Proceedings of the 31st ACM International Conference on Information and Knowledge Management, Atlanta, GA, USA, 17–21 October 2022, pp. 4464–4469. ACM (2022). https://doi.org/10.1145/3511808.3557588
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: Learning semantic representations using convolutional neural networks for web search. In: Chung, C., Broder, A.Z., Shim, K., Suel, T. (eds.) 23rd International World Wide Web Conference, WWW 2014, Companion Volume, Seoul, Republic of Korea, 7–11 April 2014, pp. 373–374. ACM (2014). https://doi.org/10.1145/2567948.2577348
Vo, D., Bagheri, E.: Self-training on refined clause patterns for relation extraction. Inf. Process. Manag. 54(4), 686–706 (2018). https://doi.org/10.1016/j.ipm.2017.02.009
Wan, S., Lan, Y., Guo, J., Xu, J., Pang, L., Cheng, X.: A deep architecture for semantic matching with multiple positional sentence representations. In: Schuurmans, D., Wellman, M.P. (eds.) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, 12–17 February 2016, pp. 2835–2841. AAAI Press (2016). http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11897
Wang, S., Jiang, J.: Learning natural language inference with LSTM. In: Knight, K., Nenkova, A., Rambow, O. (eds.) NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, 12–17 June 2016, pp. 1442–1451. The Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/n16-1170
Xiong, C., Dai, Z., Callan, J., Liu, Z., Power, R.: End-to-end neural ad-hoc ranking with kernel pooling. In: Kando, N., Sakai, T., Joho, H., Li, H., de Vries, A.P., White, R.W. (eds.) Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 7–11 August 2017, pp. 55–64. ACM (2017). https://doi.org/10.1145/3077136.3080809
Yang, P., Lin, J.: Reproducing and generalizing semantic term matching in axiomatic information retrieval. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11437, pp. 369–381. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15712-8_24
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Vo, DT., Zarrinkalam, F., Pham, B., Arabzadeh, N., Salamat, S., Bagheri, E. (2023). Neural Ad-Hoc Retrieval Meets Open Information Extraction. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_57
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