{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T04:27:14Z","timestamp":1744432034304,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030438869"},{"type":"electronic","value":"9783030438876"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-43887-6_60","type":"book-chapter","created":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T15:03:32Z","timestamp":1585321412000},"page":"670-685","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Semantically Corroborating Neural Attention for Biomedical Question Answering"],"prefix":"10.1007","author":[{"given":"Marilena","family":"Oita","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Vani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatma","family":"Oezdemir-Zaech","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"key":"60_CR1","unstructured":"Beam, A.L., et al.: Clinical concept embeddings learned from massive sources of medical data. CoRR abs\/1804.01486 (2018). http:\/\/arxiv.org\/abs\/1804.01486"},{"key":"60_CR2","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.procs.2015.12.005","volume":"73","author":"A Bouziane","year":"2015","unstructured":"Bouziane, A., Bouchiha, D., Doumi, N., Malki, M.: Question answering systems: survey and trends. Procedia Comput. Sci. 73, 366\u2013375 (2015)","journal-title":"Procedia Comput. Sci."},{"key":"60_CR3","doi-asserted-by":"crossref","unstructured":"Chandu, K., Naik, A., Chandrasekar, A., Yang, Z., Gupta, N., Nyberg, E.: Tackling biomedical text summarization: OAQA at BioaSQ 5B. In: BioNLP 2017, pp. 58\u201366 (2017)","DOI":"10.18653\/v1\/W17-2307"},{"key":"60_CR4","unstructured":"Chen, Q., Peng, Y., Lu, Z.: BioSentVec: creating sentence embeddings for biomedical texts. CoRR abs\/1810.09302 (2018). http:\/\/arxiv.org\/abs\/1810.09302"},{"key":"60_CR5","unstructured":"Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29, pp. 3504\u20133512. Curran Associates, Inc. (2016). http:\/\/papers.nips.cc\/paper\/6321-retain-an-interpretable-predictive-model-for-healthcare-using-reverse-time-attention-mechanism.pdf"},{"key":"60_CR6","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"60_CR7","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018). http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"60_CR8","doi-asserted-by":"crossref","unstructured":"Eckert, F., Neves, M.: Semantic role labeling tools for biomedical question answering: a study of selected tools on the BioASQ datasets. In: Proceedings of the 6th BioASQ Workshop A Challenge on Large-scale Biomedical Semantic Indexing and Question Answering, pp. 11\u201321 (2018)","DOI":"10.18653\/v1\/W18-5302"},{"key":"60_CR9","doi-asserted-by":"crossref","unstructured":"Hutto, C.J., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth international AAAI Conference on Weblogs and Social Media (2014)","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"60_CR10","unstructured":"Ke, J., Wang, Y., Xia, F.: Question answering system with bi-directional attention flow. CS224N Report (2017)"},{"key":"60_CR11","unstructured":"Kumar, A., et al.: Ask me anything: dynamic memory networks for natural language processing. In: International Conference on Machine Learning, pp. 1378\u20131387 (2016)"},{"key":"60_CR12","unstructured":"Kumar, A.N., et al.: Ontology-based retrieval & neural approaches for BioASQ ideal answer generation. In: Proceedings of the 6th BioASQ Workshop A Challenge on Large-scale Biomedical Semantic Indexing and Question Answering, pp. 79\u201389 (2018)"},{"key":"60_CR13","doi-asserted-by":"crossref","unstructured":"Lee, J., et al.: BioBERT: pre-trained biomedical language representation model for biomedical text mining. arXiv preprint arXiv:1901.08746 (2019)","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"60_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y., Gekakis, N., Wu, Q., Li, B., Chandu, K., Nyberg, E.: Extraction meets abstraction: ideal answer generation for biomedical questions. In: Proceedings of the 6th BioASQ Workshop A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering, pp. 57\u201365 (2018)","DOI":"10.18653\/v1\/W18-5307"},{"key":"60_CR15","unstructured":"Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. Text Summarization Branches Out (2004)"},{"key":"60_CR16","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2, pp. 3111\u20133119. Curran Associates Inc., USA (2013). http:\/\/dl.acm.org\/citation.cfm?id=2999792.2999959"},{"issue":"3","key":"60_CR17","first-page":"345","volume":"28","author":"A Mishra","year":"2016","unstructured":"Mishra, A., Jain, S.K.: A survey on question answering systems with classification. J. King Saud Univ. Comput. Inf. Sci. 28(3), 345\u2013361 (2016)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"60_CR18","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-030-12385-7_19","volume-title":"Advances in Information and Communication","author":"M Oita","year":"2020","unstructured":"Oita, M.: Reverse engineering creativity into interpretable neural networks. In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 70, pp. 235\u2013247. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-12385-7_19"},{"key":"60_CR19","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), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"60_CR20","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"60_CR21","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)","DOI":"10.18653\/v1\/D16-1264"},{"key":"60_CR22","unstructured":"Seo, M.J., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. CoRR abs\/1611.01603 (2016). http:\/\/arxiv.org\/abs\/1611.01603"},{"key":"60_CR23","doi-asserted-by":"crossref","unstructured":"Tremblay, J., et al.: Training deep networks with synthetic data: bridging the reality gap by domain randomization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 969\u2013977 (2018)","DOI":"10.1109\/CVPRW.2018.00143"},{"issue":"1","key":"60_CR24","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/s12859-015-0564-6","volume":"16","author":"G Tsatsaronis","year":"2015","unstructured":"Tsatsaronis, G., et al.: An overview of the BioASQ large-scale biomedical semantic indexing and question answering competition. BMC Bioinformatics 16(1), 138 (2015)","journal-title":"BMC Bioinformatics"},{"key":"60_CR25","unstructured":"Weissenborn, D., et al.: Jack the reader - a machine reading framework. CoRR abs\/1806.08727 (2018). http:\/\/arxiv.org\/abs\/1806.08727"},{"key":"60_CR26","unstructured":"Wiese, G., Weissenborn, D., Neves, M.L.: Neural domain adaptation for biomedical question answering. CoRR abs\/1706.03610 (2017). http:\/\/arxiv.org\/abs\/1706.03610"},{"issue":"3","key":"60_CR27","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1177\/0165551509360123","volume":"36","author":"DC Wimalasuriya","year":"2010","unstructured":"Wimalasuriya, D.C., Dou, D.: Ontology-based information extraction: an introduction and a survey of current approaches. J. Inf. Sci. 36(3), 306\u2013323 (2010). https:\/\/doi.org\/10.1177\/0165551509360123","journal-title":"J. Inf. Sci."},{"key":"60_CR28","unstructured":"Xiong, C., Merity, S., Socher, R.: Dynamic memory networks for visual and textual question answering. In: International Conference on Machine Learning, pp. 2397\u20132406 (2016)"},{"key":"60_CR29","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding (2019). http:\/\/arxiv.org\/abs\/1906.08237"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-43887-6_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T01:11:53Z","timestamp":1707786713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-43887-6_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030438869","9783030438876"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-43887-6_60","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"28 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"18% - 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":"3.04","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":"5.3","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)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}