{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T04:15:04Z","timestamp":1746591304501,"version":"3.40.5"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461461"},{"type":"electronic","value":"9783030461478"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","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":[[2020]]},"DOI":"10.1007\/978-3-030-46147-8_42","type":"book-chapter","created":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T02:03:39Z","timestamp":1588298619000},"page":"697-710","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1721-0453","authenticated-orcid":false,"given":"Cornelia","family":"Ferner","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3297-7997","authenticated-orcid":false,"given":"Stefan","family":"Wegenkittl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"42_CR1","doi-asserted-by":"crossref","unstructured":"Baldwin, T., de Marneffe, M.C., Han, B., Kim, Y.B., Ritter, A., Xu, W.: Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition. In: Proceedings of the Workshop on Noisy User-generated Text, pp. 126\u2013135 (2015)","DOI":"10.18653\/v1\/W15-4319"},{"key":"42_CR2","unstructured":"Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, pp. 3079\u20133087 (2015)"},{"key":"42_CR3","unstructured":"fact.ai: Aggregated Text Corpus of Laptop Expert Reviews with Annotated Topics (2018). https:\/\/github.com\/factai\/corpus-laptop-topic"},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Klein, D., Manning, C.D.: Conditional structure versus conditional estimation in NLP models. In: Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing, EMNLP 2002, pp. 9\u201316. ACL (2002)","DOI":"10.3115\/1118693.1118695"},{"key":"42_CR5","unstructured":"Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282\u2013289 (2001)"},{"key":"42_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071","volume-title":"Introduction to Information Retrieval","author":"CD Manning","year":"2008","unstructured":"Manning, C.D., Raghavan, P., Sch\u00fctze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)"},{"key":"42_CR7","volume-title":"Foundations of Statistical Natural Language Processing","author":"CD Manning","year":"1999","unstructured":"Manning, C.D., Sch\u00fctze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)"},{"key":"42_CR8","unstructured":"McCallum, A.: Efficiently inducing features of conditional random fields. In: Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, pp. 403\u2013410. Morgan Kaufmann Publishers Inc. (2002)"},{"key":"42_CR9","unstructured":"McCallum, A., Freitag, D., Pereira, F.C.N.: Maximum entropy Markov models for information extraction and segmentation. In: Proceedings of the Seventeenth International Conference on Machine Learning, ICML 2000, pp. 591\u2013598 (2000)"},{"key":"42_CR10","unstructured":"Medlock, B.W.: Investigating Classification for Natural Language Processing Tasks. University of Cambridge, Computer Laboratory, Technical report (2008)"},{"key":"42_CR11","unstructured":"Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: Advances in Neural Information Processing Systems, pp. 841\u2013848 (2002)"},{"key":"42_CR12","doi-asserted-by":"crossref","unstructured":"Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical Methods in Natural Language Processing-Volume 10, pp. 79\u201386. Association for Computational Linguistics (2002)","DOI":"10.3115\/1118693.1118704"},{"key":"42_CR13","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"42_CR14","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1016\/j.ipm.2005.09.002","volume":"42","author":"F Peng","year":"2006","unstructured":"Peng, F., McCallum, A.: Information extraction from research papers using conditional random fields. Inf. Process. Manage. 42(4), 963\u2013979 (2006)","journal-title":"Inf. Process. Manage."},{"key":"42_CR15","unstructured":"Petrushin, V.A.: Hidden Markov models: fundamentals and applications. In: Online Symposium for Electronics Engineer (2000)"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Pinto, D., McCallum, A., Wei, X., Croft, W.B.: Table extraction using conditional random fields. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 235\u2013242. ACM (2003)","DOI":"10.1145\/860435.860479"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 19\u201330 (2016)","DOI":"10.18653\/v1\/S16-1002"},{"key":"42_CR18","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval-2014), pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"42_CR19","unstructured":"Schwartz, A.S.: Posterior decoding methods for optimization and accuracy control of multiple alignments. Ph.D. thesis, EECS Department, University of California, Berkeley (2007)"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 134\u2013141. ACL (2003)","DOI":"10.3115\/1073445.1073473"},{"key":"42_CR21","unstructured":"Strauss, B., Toma, B., Ritter, A., de Marneffe, M.C., Xu, W.: Results of the WNUT16 named entity recognition shared task. In: Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pp. 138\u2013144 (2016)"},{"issue":"4","key":"42_CR22","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1561\/2200000013","volume":"4","author":"C Sutton","year":"2012","unstructured":"Sutton, C., McCallum, A., et al.: An introduction to conditional random fields. Found. Trends\u00ae Mach. Learn. 4(4), 267\u2013373 (2012)","journal-title":"Found. Trends\u00ae Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46147-8_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T09:22:43Z","timestamp":1746523363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46147-8_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461461","9783030461478"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46147-8_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 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)"}}]}}