{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T13:32:10Z","timestamp":1793712730765,"version":"4.6.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031560620","type":"print"},{"value":"9783031560637","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56063-7_27","type":"book-chapter","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T08:44:01Z","timestamp":1711097041000},"page":"354-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["BertPE: A BERT-Based Pre-retrieval Estimator for\u00a0Query Performance Prediction"],"prefix":"10.1007","author":[{"given":"Maryam","family":"Khodabakhsh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fattane","family":"Zarrinkalam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Negar","family":"Arabzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"27_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-030-72240-1_15","volume-title":"ECIR 2021, Part II","author":"N Arabzadeh","year":"2021","unstructured":"Arabzadeh, N., Bigdeli, A., Zihayat, M., Bagheri, E.: Query performance prediction through retrieval coherency. In: Hiemstra, D., Moens, M.F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021, Part II. LNCS, vol. 12657, pp. 193\u2013200. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72240-1_15"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Arabzadeh, N., Hamidi Rad, R., Khodabakhsh, M., Bagheri, E.: Noisy perturbations for estimating query difficulty in dense retrievers. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 3722\u20133727 (2023)","DOI":"10.1145\/3583780.3615270"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Arabzadeh, N., Khodabakhsh, M., Bagheri, E.: Bert-QPP: contextualized pre-trained transformers for query performance prediction. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2857\u20132861 (2021)","DOI":"10.1145\/3459637.3482063"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Arabzadeh, N., Seifikar, M., Clarke, C.L.A.: Unsupervised question clarity prediction through retrieved item coherency (2022)","DOI":"10.1145\/3511808.3557719"},{"key":"27_CR5","unstructured":"Arabzadeh, N., Vtyurina, A., Yan, X., Clarke, C.L.A.: Shallow pooling for sparse labels. CoRR abs\/2109.00062 (2021). https:\/\/arxiv.org\/abs\/2109.00062"},{"issue":"4","key":"27_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102248","volume":"57","author":"N Arabzadeh","year":"2020","unstructured":"Arabzadeh, N., Zarrinkalam, F., Jovanovic, J., Al-Obeidat, F., Bagheri, E.: Neural embedding-based specificity metrics for pre-retrieval query performance prediction. Inf. Process. Manag. 57(4), 102248 (2020)","journal-title":"Inf. Process. Manag."},{"key":"27_CR7","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-45442-5_10","volume-title":"Advances in Information Retrieval","author":"N Arabzadeh","year":"2020","unstructured":"Arabzadeh, N., Zarrinkalam, F., Jovanovic, J., Bagheri, E.: Neural embedding-based metrics for pre-retrieval query performance prediction. In: Jose, J., et al. (eds.) ECIR 2020, Part II. LNCS, vol. 12036, pp. 78\u201385. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45442-5_10"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Arabzadeh, N., Zarrinkalam, F., Jovanovic, J., Bagheri, E.: Geometric estimation of specificity within embedding spaces. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2109\u20132112 (2019)","DOI":"10.1145\/3357384.3358152"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Carmel, D., Yom-Tov, E.: Estimating the query difficulty for information retrieval. Synthesis Lectures on Information Concepts, Retrieval, and Services, vol. 2, no. 1, pp. 1\u201389 (2010)","DOI":"10.2200\/S00235ED1V01Y201004ICR015"},{"key":"27_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-030-99739-7_8","volume-title":"European Conference on Information Retrieval","author":"X Chen","year":"2022","unstructured":"Chen, X., He, B., Sun, L.: Groupwise query performance prediction with BERT. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 64\u201374. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99739-7_8"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Cormack, G.V., Palmer, C.R., Clarke, C.L.: Efficient construction of large test collections. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 282\u2013289 (1998)","DOI":"10.1145\/290941.291009"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D.: Overview of the TREC 2020 deep learning track. In: TREC (2020)","DOI":"10.6028\/NIST.SP.1266.deep-overview"},{"key":"27_CR13","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D.: Overview of the TREC 2020 deep learning track. CoRR abs\/2102.07662 (2021). https:\/\/arxiv.org\/abs\/2102.07662"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the TREC 2019 deep learning track. In: TREC (2019)","DOI":"10.6028\/NIST.SP.1266.deep-overview"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the TREC 2019 deep learning track. arXiv preprint arXiv:2003.07820 (2020)","DOI":"10.6028\/NIST.SP.1266.deep-overview"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D.F., Lin, J.: Overview of the TREC 2021 deep learning track. In: TREC (2021)","DOI":"10.6028\/NIST.SP.500-335.deep-overview"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Craswell, N., et al.: Overview of the TREC 2022 deep learning track. In: TREC (2022)","DOI":"10.6028\/NIST.SP.500-338.deep-overview"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Datta, S., Ganguly, D., Greene, D., Mitra, M.: Deep-QPP: a pairwise interaction-based deep learning model for supervised query performance prediction. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 201\u2013209 (2022)","DOI":"10.1145\/3488560.3498491"},{"issue":"2","key":"27_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3545112","volume":"41","author":"S Datta","year":"2022","unstructured":"Datta, S., Ganguly, D., Mitra, M., Greene, D.: A relative information gain-based query performance prediction framework with generated query variants. ACM Trans. Inf. Syst. 41(2), 1\u201331 (2022)","journal-title":"ACM Trans. Inf. Syst."},{"key":"27_CR20","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"27_CR21","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1007\/978-3-319-30671-1_38","volume-title":"Advances in Information Retrieval","author":"F Diaz","year":"2016","unstructured":"Diaz, F.: Pseudo-query reformulation. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 521\u2013532. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-30671-1_38"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Faggioli, G., et al.: Towards query performance prediction for neural information retrieval: challenges and opportunities. In: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 51\u201363 (2023)","DOI":"10.1145\/3578337.3605142"},{"key":"27_CR23","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-540-30213-1_5","volume-title":"SPIRE 2004","author":"B He","year":"2004","unstructured":"He, B., Ounis, I.: Inferring query performance using pre-retrieval predictors. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 43\u201354. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30213-1_5"},{"key":"27_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)"},{"issue":"5","key":"27_CR25","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/j.ipm.2017.04.005","volume":"53","author":"DE Losada","year":"2017","unstructured":"Losada, D.E., Parapar, J., Barreiro, A.: Multi-armed bandits for adjudicating documents in pooling-based evaluation of information retrieval systems. Inf. Process. Manag. 53(5), 1005\u20131025 (2017)","journal-title":"Inf. Process. Manag."},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Mackie, I., Dalton, J., Yates, A.: How deep is your learning: the DL-hard annotated deep learning dataset. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2335\u20132341 (2021)","DOI":"10.1145\/3404835.3463262"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Meng, C., Arabzadeh, N., Aliannejadi, M., de Rijke, M.: Query performance prediction: from ad-hoc to conversational search. arXiv preprint arXiv:2305.10923 (2023)","DOI":"10.1145\/3539618.3591919"},{"key":"27_CR28","unstructured":"Mothe, J., Tanguy, L.: Linguistic features to predict query difficulty. In: ACM SIGIR 2005 Workshop on Predicting Query Difficulty - Methods and Applications (2005)"},{"key":"27_CR29","unstructured":"Nguyen, T., Rosenberg, M., Song, X., Gao, J., Tiwary, S., Majumder, R., Deng, L.: MS MARCO: a human generated machine reading comprehension dataset. In: CoCo@ NIPS (2016)"},{"key":"27_CR30","unstructured":"Nogueira, R., Yang, W., Cho, K., Lin, J.: Multi-stage document ranking with bert (2019)"},{"key":"27_CR31","doi-asserted-by":"crossref","unstructured":"Pradeep, R., Nogueira, R., Lin, J.: The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models. arXiv preprint arXiv:2101.05667 (2021)","DOI":"10.18653\/v1\/2020.findings-emnlp.63"},{"key":"27_CR32","doi-asserted-by":"publisher","unstructured":"Qu, Y., et al.: RocketQA: an optimized training approach to dense passage retrieval for open-domain question answering. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5835\u20135847. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.466","DOI":"10.18653\/v1\/2021.naacl-main.466"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Roitman, H., Erera, S., Feigenblat, G.: A study of query performance prediction for answer quality determination. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 43\u201346 (2019)","DOI":"10.1145\/3341981.3344219"},{"issue":"3","key":"27_CR34","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.ipm.2018.10.009","volume":"56","author":"D Roy","year":"2019","unstructured":"Roy, D., Ganguly, D., Mitra, M., Jones, G.J.: Estimating gaussian mixture models in the local neighbourhood of embedded word vectors for query performance prediction. Inf. Process. Manag. 56(3), 1026\u20131045 (2019)","journal-title":"Inf. Process. Manag."},{"key":"27_CR35","doi-asserted-by":"crossref","unstructured":"Salamat, S., Arabzadeh, N., Seyedsalehi, S., Bigdeli, A., Zihayat, M., Bagheri, E.: Neural disentanglement of query difficulty and semantics. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 4264\u20134268 (2023)","DOI":"10.1145\/3583780.3615189"},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Singh, A., Ganguly, D., Datta, S., Macdonald, C.: Unsupervised query performance prediction for neural models utilising pairwise rank preferences. def 1, 2 (2023)","DOI":"10.1145\/3539618.3592082"},{"key":"27_CR37","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"27_CR38","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Zamani, H., Bendersky, M.: Multivariate representation learning for information retrieval. arXiv preprint arXiv:2304.14522 (2023)","DOI":"10.1145\/3539618.3591740"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Zerveas, G., Rekabsaz, N., Eickhoff, C.: Enhancing the ranking context of dense retrieval methods through reciprocal nearest neighbors. arXiv preprint arXiv:2305.15720 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.665"},{"key":"27_CR41","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-540-78646-7_8","volume-title":"Advances in Information Retrieval","author":"Y Zhao","year":"2008","unstructured":"Zhao, Y., Scholer, F., Tsegay, Y.: Effective pre-retrieval query performance prediction using similarity and variability evidence. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 52\u201364. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-78646-7_8"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56063-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T21:31:10Z","timestamp":1731619870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56063-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031560620","9783031560637"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56063-7_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecir2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"578","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"110","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31 (Tracks: Workshop, Tutorial, Industry, Doctoral Consortium)","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}