{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:42:29Z","timestamp":1771299749270,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030721121","type":"print"},{"value":"9783030721138","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72113-8_31","type":"book-chapter","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T12:03:02Z","timestamp":1616760182000},"page":"467-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["CEQE: Contextualized Embeddings for Query Expansion"],"prefix":"10.1007","author":[{"given":"Shahrzad","family":"Naseri","sequence":"first","affiliation":[]},{"given":"Jeffrey","family":"Dalton","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Yates","sequence":"additional","affiliation":[]},{"given":"James","family":"Allan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Akkalyoncu Yilmaz, Z., Yang, W., Zhang, H., Lin, J.: Cross-domain modeling of sentence-level evidence for document retrieval. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, November 2019","DOI":"10.18653\/v1\/D19-1352"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Cao, G., Nie, J.Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2008, ACM, New York, NY, USA (2008)","DOI":"10.1145\/1390334.1390377"},{"key":"31_CR3","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D.: Overview of the trec 2019 deep learning track. In: Proceedings of The Twenty-Eight Text REtrieval Conference, TREC 2019, Gaithersburg, Maryland, USA, November 13\u201315, 2019 (2019)"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Dai, Z., Callan, J.: Deeper text understanding for IR with contextual neural language modeling. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2019, Association for Computing Machinery, New York, NY, USA (2019)","DOI":"10.1145\/3331184.3331303"},{"key":"31_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-030-15712-8_19","volume-title":"Advances in Information Retrieval","author":"J Dalton","year":"2019","unstructured":"Dalton, J., Naseri, S., Dietz, L., Allan, J.: Local and global query expansion for hierarchical complex topics. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11437, pp. 290\u2013303. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-15712-8_19"},{"key":"31_CR6","unstructured":"Devlin, J., Chang, M.W., 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, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, June 2019"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Diaz, F., Mitra, B., Craswell, N.: Query expansion with locally-trained word embeddings. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2016)","DOI":"10.18653\/v1\/P16-1035"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Gao, L., Dai, Z., Fan, Z., Callan, J.: Complementing lexical retrieval with semantic residual embedding. arXiv preprint arXiv:2004.13969 (2020)","DOI":"10.1007\/978-3-030-72113-8_10"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Huston, S., Croft, W.B.: Parameters learned in the comparison of retrieval models using term dependencies. University of Massachusetts, Ir (2014)","DOI":"10.1145\/2661829.2661894"},{"key":"31_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-030-15719-7_26","volume-title":"Advances in Information Retrieval","author":"A Imani","year":"2019","unstructured":"Imani, A., Vakili, A., Montazer, A., Shakery, A.: Deep neural networks for query eexpansion using word embeddings. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11438, pp. 203\u2013210. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-15719-7_26"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Khattab, O., Zaharia, M.: ColBERT: efficient and effective passage search via contextualized late interaction over BERT. In: Proceedings of the 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020) (2020)","DOI":"10.1145\/3397271.3401075"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Kuzi, S., Shtok, A., Kurland, O.: Query expansion using word embeddings. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM (2016)","DOI":"10.1145\/2983323.2983876"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Lavrenko, V., Croft, W.B.: Relevance based language models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2001, ACM, New York, NY, USA (2001)","DOI":"10.1145\/383952.383972"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: NPRF: a neural pseudo relevance feedback framework for ad-hoc information retrieval. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (2018)","DOI":"10.18653\/v1\/D18-1478"},{"key":"31_CR15","unstructured":"Li, C., Yates, A., MacAvaney, S., He, B., Sun, Y.: Parade: passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093 (2020)"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Lv, Y., Zhai, C.: A comparative study of methods for estimating query language models with pseudo feedback. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM (2009)","DOI":"10.1145\/1645953.1646259"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"MacAvaney, S., Nardini, F.M., Perego, R., Tonellotto, N., Goharian, N., Frieder, O.: Expansion via prediction of importance with contextualization. arXiv preprint arXiv:2004.14245 (2020)","DOI":"10.1145\/3397271.3401262"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"MacAvaney, S., Yates, A., Cohan, A., Goharian, N.: CEDR: contextualized embeddings for document ranking. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21\u201325 (2019)","DOI":"10.1145\/3331184.3331317"},{"key":"31_CR19","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)"},{"key":"31_CR20","unstructured":"Naseri, S., Foley, J., Allan, J., O\u2019Connor, B.: Exploring summary-expanded entity embeddings for entity retrieval. In: CEUR Workshop Proceedings (2018)"},{"key":"31_CR21","unstructured":"Nogueira, R., Cho, K.: Passage re-ranking with bert. arXiv preprint arXiv:1901.04085 (2019)"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Nogueira, R., Jiang, Z., Lin, J.: Document ranking with a pretrained sequence-to-sequence model. arXiv preprint arXiv:2003.06713 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.63"},{"key":"31_CR23","unstructured":"Nogueira, R., Yang, W., Lin, J., Cho, K.: Document expansion by query prediction. arXiv preprint arXiv:1904.08375 (2019)"},{"key":"31_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-45442-5_37","volume-title":"Advances in Information Retrieval","author":"R Padaki","year":"2020","unstructured":"Padaki, R., Dai, Z., Callan, J.: Rethinking query expansion for BERT reranking. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 297\u2013304. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45442-5_37"},{"key":"31_CR25","unstructured":"Padigela, H., Zamani, H., Croft, W.B.: Investigating the successes and failures of bert for passage re-ranking. arXiv preprint arXiv:1905.01758 (2019)"},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, June 2018","DOI":"10.18653\/v1\/N18-1202"},{"key":"31_CR28","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Ruder, S., Smith, N.A.: To tune or not to tune? adapting pretrained representations to diverse tasks. In: RepL4NLP@ACL (2019)","DOI":"10.18653\/v1\/W19-4302"},{"key":"31_CR29","unstructured":"Qiao, Y., Xiong, C., Liu, Z., Liu, Z.: Understanding the behaviors of bert in ranking. arXiv preprint arXiv:1904.07531 (2019)"},{"key":"31_CR30","unstructured":"Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System: Experiments in Automatic Document Processing, chap. 14, pp. 313\u2013323. Prentice-Hall Series in Automatic Computation, Prentice-Hall, Englewood Cliffs NJ (1971)"},{"key":"31_CR31","unstructured":"Roy, D., Paul, D., Mitra, M., Garain, U.: Using word embeddings for automatic query expansion, July 2016"},{"key":"31_CR32","doi-asserted-by":"crossref","unstructured":"Schuster, M., Nakajima, K.: Japanese and Korean voice search. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2012)","DOI":"10.1109\/ICASSP.2012.6289079"},{"key":"31_CR33","unstructured":"Xiong, L., et al.: Approximate nearest neighbor negative contrastive learning for dense text retrieval. In: International Conference on Learning Representations (2021)"},{"key":"31_CR34","doi-asserted-by":"crossref","unstructured":"Yang, W., Xie, Y., Lin, A., Li, X., Tan, L., Xiong, K., Li, M., Lin, J.: End-to-end open-domain question answering with bertserini. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations) (2019)","DOI":"10.18653\/v1\/N19-4013"},{"key":"31_CR35","unstructured":"Yilmaz, Z.A., Wang, S., Yang, W., Zhang, H., Lin, J.: Applying bert to document retrieval with birch. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pp. 19\u201324 (2019)"},{"key":"31_CR36","doi-asserted-by":"crossref","unstructured":"Zamani, H., Croft, W.B.: Embedding-based query language models. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval. ACM (2016)","DOI":"10.1145\/2970398.2970405"},{"key":"31_CR37","doi-asserted-by":"crossref","unstructured":"Zamani, H., Croft, W.B.: Relevance-based word embedding. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 505\u2013514. ACM (2017)","DOI":"10.1145\/3077136.3080831"},{"key":"31_CR38","doi-asserted-by":"crossref","unstructured":"Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the Tenth International Conference on Information and Knowledge Management. CIKM 2001, ACM, ACM, New York, NY, USA (2001). http:\/\/doi.acm.org\/10.1145\/502585.502654","DOI":"10.1145\/502653.502654"},{"key":"31_CR39","unstructured":"Zhan, J., Mao, J., Liu, Y., Zhang, M., Ma, S.: Repbert: contextualized text embeddings for first-stage retrieval. arXiv preprint arXiv:2006.15498 (2020)"},{"key":"31_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Generic intent representation in web search. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, 21\u201325 July, Paris, France (2019)","DOI":"10.1145\/3331184.3331198"},{"key":"31_CR41","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Hui, K., He, B., Han, X., Sun, L., Yates, A.: Bert-QE: Contextualized query expansion for document re-ranking. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp. 4718\u20134728 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.424"}],"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\/978-3-030-72113-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:03:39Z","timestamp":1710241419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72113-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030721121","9783030721138"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72113-8_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 March 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecir2021.eu\/","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":"436","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":"50","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":"39","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":"11% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}