{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:47:08Z","timestamp":1743040028046,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031416811"},{"type":"electronic","value":"9783031416828"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-41682-8_16","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:02:59Z","timestamp":1692342179000},"page":"249-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Triple-Assisted Learning for\u00a0Question Answering Passage Re-ranking"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4182-3906","authenticated-orcid":false,"given":"Dinesh","family":"Nagumothu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0579-8018","authenticated-orcid":false,"given":"Bahadorreza","family":"Ofoghi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2313-8603","authenticated-orcid":false,"given":"Peter W.","family":"Eklund","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"16_CR1","unstructured":"Callan, J., Hoy, M., Yoo, C., Zhao, L.: Clueweb09 data set (2009), https:\/\/lemurproject.org\/clueweb09\/ Accessed 28 Apr 2023"},{"key":"16_CR2","unstructured":"Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E.M.: Overview of the TREC 2019 Deep Learning Track, https:\/\/arxiv.org\/abs\/2003.07820 Accessed 28 Apr 2023"},{"key":"16_CR3","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, Vol 1. pp. 4171\u20134186 (2019)"},{"key":"16_CR4","unstructured":"Dhingra, B., Mazaitis, K., Cohen, W.W.: Quasar: Datasets for question answering by search and reading (2017), https:\/\/arxiv.org\/abs\/1707.03904"},{"key":"16_CR5","doi-asserted-by":"publisher","unstructured":"Dong, Q., et al.: Incorporating explicit knowledge in pre-trained language models for passage re-ranking. In: Proceedings of the 45th International ACM SIGIR Conference, pp. 1490\u20131501. SIGIR \u201922, ACM, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3477495.3531997","DOI":"10.1145\/3477495.3531997"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Gao, L., Dai, Z., Callan, J.: Understanding BERT rankers under distillation. In: Proc of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. pp. 149\u2013152 (2020)","DOI":"10.1145\/3409256.3409838"},{"key":"16_CR7","doi-asserted-by":"publisher","unstructured":"Izacard, G., Grave, E.: Leveraging passage retrieval with generative models for open domain question answering. In: Proceeding of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. pp. 874\u2013880. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.74","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"16_CR8","doi-asserted-by":"publisher","unstructured":"Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. In: Proceeding of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 6769\u20136781. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.550","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Khattab, O., Zaharia, M.: ColBERT: Efficient and effective passage search via contextualized late interaction over bert. In: Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 39\u201348. SIGIR \u201920, ACM, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3397271.3401075","DOI":"10.1145\/3397271.3401075"},{"key":"16_CR10","doi-asserted-by":"publisher","unstructured":"Kolluru, K., Adlakha, V., Aggarwal, S., Mausam, Chakrabarti, S.: OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction. In: Proceeding of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 3748\u20133761. Association for Computational Linguistics, Online (Nov 2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.306","DOI":"10.18653\/v1\/2020.emnlp-main.306"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., Chen, W.: Reader-guided passage reranking for open-domain question answering. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp. 344\u2013350 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.29"},{"key":"16_CR12","unstructured":"Nguyen, T., et al.: MS MARCO: A human generated machine reading comprehension dataset. In: CoCo@ NIPs (2016)"},{"key":"16_CR13","unstructured":"Nogueira, R., Cho, K.: Passage re-ranking with BERT (2019), https:\/\/arxiv.org\/abs\/1901.04085"},{"key":"16_CR14","unstructured":"Nogueira, R., Yang, W., Cho, K., Lin, J.: Multi-stage document ranking with BERT (2019), https:\/\/arxiv.org\/abs\/1910.14424"},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Ofoghi, B.: Linguistic characterization of answer passages for fact-seeking question answering. In: Proceedings of the 37th ACM\/SIGAPP Symposium on Applied Computing. p. 821\u2013828. SAC \u201922, ACM, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3477314.3506999","DOI":"10.1145\/3477314.3506999"},{"key":"16_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108574","volume":"244","author":"B Ofoghi","year":"2022","unstructured":"Ofoghi, B., Mahdiloo, M., Yearwood, J.: Data envelopment analysis of linguistic features and passage relevance for open-domain question answering. Knowl.-Based Syst. 244, 108574 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.108574","journal-title":"Knowl.-Based Syst."},{"key":"16_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-030-85529-1_23","volume-title":"Modeling Decisions for Artificial Intelligence","author":"B Ofoghi","year":"2021","unstructured":"Ofoghi, B., Zarnegar, A.: Answer Passage Ranking Enhancement Using Shallow Linguistic Features. In: Torra, V., Narukawa, Y. (eds.) MDAI 2021. LNCS (LNAI), vol. 12898, pp. 286\u2013298. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85529-1_23"},{"key":"16_CR18","doi-asserted-by":"publisher","unstructured":"Oguz, B., et al.: UniK-QA: Unified representations of structured and unstructured knowledge for open-domain question answering. In: Findings of the Association for Computational Linguistics: NAACL 2022. pp. 1535\u20131546. Association for Computational Linguistics, Seattle, United States (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.115","DOI":"10.18653\/v1\/2022.findings-naacl.115"},{"key":"16_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/978-3-030-99736-6_44","volume-title":"Advances in Information Retrieval","author":"R Pradeep","year":"2022","unstructured":"Pradeep, R., Liu, Y., Zhang, X., Li, Y., Yates, A., Lin, J.: Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for Reranking. In: Hagen, M., Verberne, S., Macdonald, C., Seifert, C., Balog, K., N\u00f8rv\u00e5g, K., Setty, V. (eds.) ECIR 2022. LNCS, vol. 13185, pp. 655\u2013670. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99736-6_44"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Qu, Y., et al.: RocketQA: an optimized training approach to dense passage retrieval for open-domain question answering. In: In Proceedings of NAACL (2021)","DOI":"10.18653\/v1\/2021.naacl-main.466"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Making monolingual sentence embeddings multilingual using knowledge distillation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2020), https:\/\/arxiv.org\/abs\/2004.09813","DOI":"10.18653\/v1\/2020.emnlp-main.365"},{"key":"16_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"16_CR23","unstructured":"Wang, S., et al.: Ernie 3.0 titan: exploring larger-scale knowledge enhanced pre-training for language understanding and generation (2021), https:\/\/arxiv.org\/abs\/2112.12731"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Yan, M., Li, C., Bi, B., Wang, W., Huang, S.: A unified pretraining framework for passage ranking and expansion. In: Proceeding of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 4555\u20134563 (2021)","DOI":"10.1609\/aaai.v35i5.16584"},{"key":"16_CR25","doi-asserted-by":"publisher","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: Proceeding of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). pp. 72\u201377. Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-4013,https:\/\/aclanthology.org\/N19-4013","DOI":"10.18653\/v1\/N19-4013"},{"key":"16_CR26","doi-asserted-by":"publisher","unstructured":"Yu, D., et al.: KG-FiD: infusing knowledge graph in fusion-in-decoder for open-domain question answering. In: Proc of the 60th Annual Meeting of the Association for Computational Linguistics, Vol. 1, pp. 4961\u20134974. Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.340","DOI":"10.18653\/v1\/2022.acl-long.340"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition - ICDAR 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41682-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:20:24Z","timestamp":1692343224000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41682-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416811","9783031416828"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41682-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"19 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Jos\u00e9, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdar2023.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":"316","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":"154","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":"0","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":"49% - 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":"2.89","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":"1.50","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":"Number and type of other papers accepted : IJDAR track papers","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)"}}]}}