{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T18:43:09Z","timestamp":1769107389205,"version":"3.49.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030722395","type":"print"},{"value":"9783030722401","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":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72240-1_1","type":"book-chapter","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T14:49:01Z","timestamp":1617288541000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cross-Domain Retrieval in the Legal and Patent Domains: A Reproducibility Study"],"prefix":"10.1007","author":[{"given":"Sophia","family":"Althammer","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"Hofst\u00e4tter","sequence":"additional","affiliation":[]},{"given":"Allan","family":"Hanbury","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","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), pp. 3490\u20133496. Association for Computational Linguistics, Hong Kong, China, November 2019. https:\/\/doi.org\/10.18653\/v1\/D19-1352. https:\/\/www.aclweb.org\/anthology\/D19-1352","DOI":"10.18653\/v1\/D19-1352"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"Bhattacharya, P., et al.: Fire 2019 AILA track: artificial intelligence for legal assistance. In: Proceedings of the 11th Forum for Information Retrieval Evaluation, FIRE 2019, pp. 4\u20136. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3368567.3368587","DOI":"10.1145\/3368567.3368587"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724\u20131734. Association for Computational Linguistics, Doha, October 2014. https:\/\/doi.org\/10.3115\/v1\/D14-1179. https:\/\/www.aclweb.org\/anthology\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Cormack, G., Grossman, M.: Autonomy and reliability of continuous active learning for technology-assisted review, April 2015","DOI":"10.1145\/2766462.2767771"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Cormack, G.V., Grossman, M.R.: Evaluation of machine-learning protocols for technology-assisted review in electronic discovery. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, pp. 153\u2013162. Association for Computing Machinery, New York (2014). https:\/\/doi.org\/10.1145\/2600428.2609601","DOI":"10.1145\/2600428.2609601"},{"key":"1_CR6","doi-asserted-by":"publisher","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), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/www.aclweb.org\/anthology\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Gao, L., Dai, Z., Callan, J.: Modularized transfomer-based ranking framework. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.342"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Hedin, B., Zaresefat, S., Baron, J., Oard, D.: Overview of the TREC 2009 legal track. In: The Eighteenth Text Retrieval Conference (TREC 2009) Proceedings, January 2009","DOI":"10.6028\/NIST.SP.500-278.legal-overview"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"1_CR10","unstructured":"Hofst\u00e4tter, S., Hanbury, A.: Let\u2019s measure run time! Extending the IR replicability infrastructure to include performance aspects. In: Proceedings of OSIRRC (2019)"},{"key":"1_CR11","unstructured":"Hofst\u00e4tter, S., Zlabinger, M., Hanbury, A.: Interpretable & time-budget-constrained contextualization for re-ranking. In: Proceedings of ECAI (2020)"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Lee, K., Chang, M.W., Toutanova, K.: Latent retrieval for weakly supervised open domain question answering. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6086\u20136096. Association for Computational Linguistics, Florence, July 2019. https:\/\/doi.org\/10.18653\/v1\/P19-1612. https:\/\/www.aclweb.org\/anthology\/P19-1612","DOI":"10.18653\/v1\/P19-1612"},{"key":"1_CR13","doi-asserted-by":"publisher","unstructured":"MacAvaney, S., Cohan, A., Goharian, N.: SLEDGE-Z: a zero-shot baseline for COVID-19 literature search. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4171\u20134179. Association for Computational Linguistics, November 2020. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.341. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.341","DOI":"10.18653\/v1\/2020.emnlp-main.341"},{"key":"1_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-642-40802-1_25","volume-title":"Information Access Evaluation. Multilinguality, Multimodality, and Visualization","author":"F Piroi","year":"2013","unstructured":"Piroi, F., Lupu, M., Hanbury, A.: Overview of CLEF-IP 2013 lab. In: Forner, P., M\u00fcller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 232\u2013249. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40802-1_25"},{"key":"1_CR15","unstructured":"Piroi, F., Lupu, M., Hanbury, A., Zenz, V.: CLEF-IP 2011: retrieval in the intellectual property domain, January 2011"},{"key":"1_CR16","unstructured":"Piroi, F., Tait, J.: CLEF-IP 2010: retrieval experiments in the intellectual property domain (2010)"},{"key":"1_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-030-58790-1_3","volume-title":"New Frontiers in Artificial Intelligence","author":"J Rabelo","year":"2020","unstructured":"Rabelo, J., Kim, M.-Y., Goebel, R., Yoshioka, M., Kano, Y., Satoh, K.: A summary of the COLIEE 2019 competition. In: Sakamoto, M., Okazaki, N., Mineshima, K., Satoh, K. (eds.) JSAI-isAI 2019. LNCS (LNAI), vol. 12331, pp. 34\u201349. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58790-1_3"},{"key":"1_CR18","doi-asserted-by":"publisher","unstructured":"Robertson, S., Zaragoza, H.: The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retr. 3(4), 333\u2013389 (2009). https:\/\/doi.org\/10.1561\/1500000019","DOI":"10.1561\/1500000019"},{"key":"1_CR19","unstructured":"Rossi, J., Kanoulas, E.: Legal information retrieval with generalized language models (2019)"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Shao, Y., et al.: BERT-PLI: modeling paragraph-level interactions for legal case retrieval. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 3501\u20133507. International Joint Conferences on Artificial Intelligence Organization, July 2020. Main track","DOI":"10.24963\/ijcai.2020\/484"},{"key":"1_CR21","doi-asserted-by":"publisher","unstructured":"Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 623\u2013632. Association for Computing Machinery, New York (2007). https:\/\/doi.org\/10.1145\/1321440.1321528","DOI":"10.1145\/1321440.1321528"},{"key":"1_CR22","doi-asserted-by":"publisher","unstructured":"Tran, V., Nguyen, M.L., Satoh, K.: Building legal case retrieval systems with lexical matching and summarization using a pre-trained phrase scoring model. In: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law, ICAIL 2019, pp. 275\u2013282. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3322640.3326740","DOI":"10.1145\/3322640.3326740"},{"key":"1_CR23","doi-asserted-by":"publisher","unstructured":"Urbano, J., Lima, H., Hanjalic, A.: Statistical significance testing in information retrieval: an empirical analysis of type i, type ii and type iii errors. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 505\u2013514. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3331184.3331259","DOI":"10.1145\/3331184.3331259"},{"key":"1_CR24","unstructured":"Xiong, C., et al.: CMT in TREC-COVID round 2: mitigating the generalization gaps from web to special domain search. In: ArXiv preprint (2020)"},{"key":"1_CR25","doi-asserted-by":"publisher","unstructured":"Yang, W., et al.: 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), pp. 72\u201377. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-4013","DOI":"10.18653\/v1\/N19-4013"},{"key":"1_CR26","unstructured":"Zhang, Y., Nie, P., Geng, X., Ramamurthy, A., Song, L., Jiang, D.: DC-BERT: decoupling question and document for efficient contextual encoding (2020)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72240-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T10:00:41Z","timestamp":1724752841000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72240-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030722395","9783030722401"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72240-1_1","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":"30 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)"}}]}}