{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:26:07Z","timestamp":1743110767634,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031398209"},{"type":"electronic","value":"9783031398216"}],"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\/978-3-031-39821-6_25","type":"book-chapter","created":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T21:01:25Z","timestamp":1692133285000},"page":"310-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpreting Deep Text Quantification Models"],"prefix":"10.1007","author":[{"given":"YunQi","family":"Bang","sequence":"first","affiliation":[]},{"given":"Mohammed","family":"Khaleel","sequence":"additional","affiliation":[]},{"given":"Wallapak","family":"Tavanapong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"25_CR1","unstructured":"Ali, A., Schnake, T., Eberle, O., Montavon, G., M\u00fcller, K.R., Wolf, L.: XAI for transformers: better explanations through conservative propagation. In: ICML (2022)"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Arras, L., Horn, F., Montavon, G., M\u00fcller, K.R., Samek, W.: \u201cWhat is relevant in a text document?\u201d: an interpretable machine learning approach. PloS One 12(8), e0181142 (2017)","DOI":"10.1371\/journal.pone.0181142"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Arras, L., Montavon, G., M\u00fcller, K.R., Samek, W.: Explaining recurrent neural network predictions in sentiment analysis. In: Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 159\u2013168. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/W17-5221"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)","DOI":"10.1371\/journal.pone.0130140"},{"issue":"2","key":"25_CR5","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.patcog.2014.07.032","volume":"48","author":"J Barranquero","year":"2015","unstructured":"Barranquero, J., D\u00edez, J., del Coz, J.J.: Quantification-oriented learning based on reliable classifiers. Pattern Recognit. 48(2), 591\u2013604 (2015)","journal-title":"Pattern Recognit."},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Bella, A., Ferri, C., Hern\u00e1ndez-Orallo, J., Ram\u00edrez-Quintana, M.J.: Quantification via probability estimators, pp. 737\u2013742. IEEE (2010)","DOI":"10.1109\/ICDM.2010.75"},{"key":"25_CR7","series-title":"The Information Retrieval Series","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-031-20467-8_4","volume-title":"Learning to Quantify","author":"A Esuli","year":"2022","unstructured":"Esuli, A., Fabris, A., Moreo, A., Sebastiani, F.: Methods for learning to quantify. In: Esuli, A., Fabris, A., Moreo, A., Sebastiani, F. (eds.) Learning to Quantify. The Information Retrieval Series, vol. 47, pp. 55\u201385. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20467-8_4"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Esuli, A., Moreo Fern\u00e1ndez, A., Sebastiani, F.: A recurrent neural network for sentiment quantification. In: Proceedings of ACM International Conference on Information and Knowledge Management, Torino, Italy. Association for Computing Machinery (2018)","DOI":"10.1145\/3269206.3269287"},{"key":"25_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1007\/11564096_55","volume-title":"Machine Learning: ECML 2005","author":"G Forman","year":"2005","unstructured":"Forman, G.: Counting positives accurately despite inaccurate classification. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 564\u2013575. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564096_55"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Forman, G.: Quantifying trends accurately despite classifier error and class imbalance. In: Proceedings of ACM SIGKDD, pp. 157\u2013166 (2006)","DOI":"10.1145\/1150402.1150423"},{"key":"25_CR11","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ins.2012.05.028","volume":"218","author":"V Gonz\u00e1lez-Castro","year":"2013","unstructured":"Gonz\u00e1lez-Castro, V., Alaiz-Rodr\u00edguez, R., Alegre, E.: Class distribution estimation based on the Hellinger distance. Inf. Sci. 218, 146\u2013164 (2013)","journal-title":"Inf. Sci."},{"key":"25_CR12","unstructured":"Jerzak, C.T., King, G., Strezhnev, A.: An improved method of automated nonparametric content analysis for social science. Polit. Anal. 1(17) (2019)"},{"issue":"1","key":"25_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-022-00583-6","volume":"9","author":"M Khaleel","year":"2022","unstructured":"Khaleel, M., Qi, L., Tavanapong, W., Wong, J., Sukul, A., Peterson, D.A.M.: IDC: quantitative evaluation benchmark of interpretation methods for deep text classification models. J. Big Data 9(1), 1\u201314 (2022)","journal-title":"J. Big Data"},{"key":"25_CR14","unstructured":"Maas, A., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142\u2013150 (2011)"},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Maletzke, A., Reis, D.D., Hassan, W., Batista, G.: Accurately quantifying under score variability. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 1228\u20131233 (2021). https:\/\/doi.org\/10.1109\/ICDM51629.2021.00149","DOI":"10.1109\/ICDM51629.2021.00149"},{"key":"25_CR16","unstructured":"Martino, G., Gao, W., Sebastiani, F.: Ordinal text quantification. In: Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 937\u2013940 (2016)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Milli, L., Monreale, A., Rossetti, G., Giannotti, F., Pedreschi, D., Sebastiani, F.: Quantification trees. In: IEEE International Conference on Data Mining, pp. 528\u2013536 (2013)","DOI":"10.1109\/ICDM.2013.122"},{"key":"25_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-030-28954-6_10","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"G Montavon","year":"2019","unstructured":"Montavon, G., Binder, A., Lapuschkin, S., Samek, W., M\u00fcller, K.-R.: Layer-wise relevance propagation: an overview. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 193\u2013209. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_10"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Moreo, A., Esuli, A., Sebastiani, F.: QuaPy: a python-based framework for quantification. In: Proceedings of ACM International Conference on Information & Knowledge Management, pp. 4534\u20134543 (2021)","DOI":"10.1145\/3459637.3482015"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Moreo, A., Sebastiani, F.: Tweet sentiment quantification: an experimental re-evaluation. PLoS One 17(9), e0263449 (2022)","DOI":"10.1371\/journal.pone.0263449"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-G\u00e1llego, P., Casta\u00f1o, A., Ram\u00f3n Quevedo, J., del Coz, J.J.: Dynamic ensemble selection for quantification tasks. Inf. Fusion 45, 1\u201315 (2019)","DOI":"10.1016\/j.inffus.2018.01.001"},{"key":"25_CR22","unstructured":"Qi, L.: Quantification learning with deep neural networks (2021)"},{"key":"25_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-030-67658-2_14","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"L Qi","year":"2021","unstructured":"Qi, L., Khaleel, M., Tavanapong, W., Sukul, A., Peterson, D.: A framework for deep quantification learning. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12457, pp. 232\u2013248. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67658-2_14"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Qi, L., Li, R., Wong, J., Tavanapong, W., Peterson, D.A.: Social media in state politics: mining policy agendas topics. In: Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 274\u2013277 (2017)","DOI":"10.1145\/3110025.3110097"},{"issue":"1","key":"25_CR25","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1162\/089976602753284446","volume":"14","author":"M Saerens","year":"2002","unstructured":"Saerens, M., Latinne, P., Decaestecker, C.: Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure. Neural Comput. 14(1), 21\u201341 (2002)","journal-title":"Neural Comput."},{"issue":"1","key":"25_CR26","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3390\/app12010136","volume":"12","author":"I Ullah","year":"2021","unstructured":"Ullah, I., Rios, A., Gala, V., Mckeever, S.: Explaining deep learning models for tabular data using layer-wise relevance propagation. Appl. Sci. 12(1), 136 (2021)","journal-title":"Appl. Sci."},{"key":"25_CR27","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39821-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:48:48Z","timestamp":1710269328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39821-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031398209","9783031398216"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39821-6_25","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":"16 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Penang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","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":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/dexa2023","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"155","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":"49","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":"35","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":"32% - 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":"4","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":"For the workshops 7 full and 3 short papers have been accepted from 20 submissions","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)"}}]}}