{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T17:26:24Z","timestamp":1757611584397,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863302"},{"type":"electronic","value":"9783030863319"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86331-9_10","type":"book-chapter","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T02:05:57Z","timestamp":1630721157000},"page":"145-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GSSF: A Generative Sequence Similarity Function Based on a Seq2Seq Model for Clustering Online Handwritten Mathematical Answers"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9238-8601","authenticated-orcid":false,"given":"Huy Quang","family":"Ung","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2556-9191","authenticated-orcid":false,"given":"Cuong Tuan","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4751-1302","authenticated-orcid":false,"given":"Hung Tuan","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-156X","authenticated-orcid":false,"given":"Masaki","family":"Nakagawa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Mahdavi, M., Zanibbi, R., Mouchere, H., Viard-Gaudin, C., Garain, U.: CROHME+TFD: competition on recognition of handwritten mathematical expressions and typeset formula detection. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 1533\u20131538 (2019)","DOI":"10.1109\/ICDAR.2019.00247"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1145\/1015706.1015741","volume":"23","author":"JJ LaViola","year":"2004","unstructured":"LaViola, J.J., Zeleznik, R.C.: MathPad2: a system for the creation and exploration of mathematical sketches. ACM Trans. Graph. 23, 432\u2013440 (2004)","journal-title":"ACM Trans. Graph."},{"key":"10_CR3","unstructured":"Chan, K.F., Yeung, D.Y.: PenCalc: a novel application of on-line mathematical expression recognition technology. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 774\u2013778 (2001)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"O\u2019Connell, T., Li, C., Miller, T.S., Zeleznik, R.C., LaViola, J.J.: A usability evaluation of AlgoSketch: a pen-based application for mathematics. In: Proceedings of Eurographics Symposium on Sketch-Based Interfaces Model, pp. 149\u2013157 (2009)","DOI":"10.1145\/1572741.1572767"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Khuong, V.T.M., Phan, K.M., Ung, H.Q., Nguyen, C.T., Nakagawa, M.: Clustering of handwritten mathematical expressions for computer-assisted marking. IEICE Trans. Inf. Syst. E104.D, 275\u2013284 (2021). https:\/\/doi.org\/10.1587\/transinf.2020EDP7087","DOI":"10.1587\/transinf.2020EDP7087"},{"key":"10_CR6","unstructured":"Ung, H.Q., Khuong, V.T.M., Le, A.D., Nguyen, C.T., Nakagawa, M.: Bag-of-features for clustering online handwritten mathematical expressions. In: Proceedings of International Conference on Pattern Recognition and Artificial Intelligence, pp. 127\u2013132 (2018)"},{"key":"10_CR7","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.patrec.2019.12.015","volume":"131","author":"CT Nguyen","year":"2020","unstructured":"Nguyen, C.T., Khuong, V.T.M., Nguyen, H.T., Nakagawa, M.: CNN based spatial classification features for clustering offline handwritten mathematical expressions. Pattern Recognit. Lett. 131, 113\u2013120 (2020)","journal-title":"Pattern Recognit. Lett."},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1109\/TKDE.2007.1037","volume":"19","author":"D Fran\u00e7ois","year":"2007","unstructured":"Fran\u00e7ois, D., Wertz, V., Verieysen, M.: The concentration of fractional distances. IEEE Trans. Knowl. Data Eng. 19, 873\u2013886 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Cummins, R., Zhang, M., Briscoe, T.: Constrained multi-task learning for automated essay scoring. In: Proceedings of Annual Meeting Association and Computing Linguistics, pp. 789\u2013799 (2016)","DOI":"10.18653\/v1\/P16-1075"},{"key":"10_CR10","first-page":"319","volume":"2","author":"V Salvatore","year":"2003","unstructured":"Salvatore, V., Francesca, N., Alessandro, C.: An Overview of current research on automated essay grading. J. Inf. Technol. Educ. Res. 2, 319\u2013330 (2003)","journal-title":"J. Inf. Technol. Educ. Res."},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Ishioka, T., Kameda, M.: Automated Japanese essay scoring system: jess. In: Proceedings of International Workshop Database Expert Systema and Applications, pp. 4\u20138 (2004)","DOI":"10.1109\/DEXA.2004.1333440"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.artint.2007.06.005","volume":"172","author":"S Srihari","year":"2008","unstructured":"Srihari, S., Collins, J., Srihari, R., Srinivasan, H., Shetty, S., Brutt-Griffler, J.: Automatic scoring of short handwritten essays in reading comprehension tests. Artif. Intell. 172, 300\u2013324 (2008)","journal-title":"Artif. Intell."},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1162\/tacl_a_00236","volume":"1","author":"S Basu","year":"2013","unstructured":"Basu, S., Jacobs, C., Vanderwende, L.: Powergrading: a clustering approach to amplify human effort for short answer grading. Trans. Assoc. Comput. Linguist. 1, 391\u2013402 (2013)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Brooks, M., Basu, S., Jacobs, C., Vanderwende, L.: Divide and correct: using clusters to grade short answers at scale. In: Proceedings of ACM Conference on Learning @ Scale, pp. 89\u201398 (2014)","DOI":"10.1145\/2556325.2566243"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TMM.2018.2844689","volume":"21","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Du, J., Dai, L.: Track, attend, and parse (TAP): an end-to-end framework for online handwritten mathematical expression recognition. IEEE Trans. Multimed. 21, 221\u2013233 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"10_CR16","doi-asserted-by":"publisher","unstructured":"Hong, Z., You, N., Tan, J., Bi, N.: Residual BiRNN based Seq2Seq model with transition probability matrix for online handwritten mathematical expression recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 635\u2013640 (2019). https:\/\/doi.org\/10.1109\/ICDAR.2019.00107","DOI":"10.1109\/ICDAR.2019.00107"},{"key":"10_CR17","unstructured":"Khuong, V.T.M.: A Synthetic Dataset for Clustering Handwritten Math Expression TUAT (Dset_Mix) - TC-11. http:\/\/tc11.cvc.uab.es\/datasets\/Dset_Mix_1"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Phan, K.M., Khuong, V.T.M., Ung, H.Q., Nakagawa, M.: Generating synthetic handwritten mathematical expressions from a LaTeX sequence or a MathML script. In: Proceedings of International Conference on Document Analysis and Recognition, pp. 922\u2013927 (2020)","DOI":"10.1109\/ICDAR.2019.00152"},{"key":"10_CR19","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1564\/tme_v26.3.04","volume":"26","author":"F Yasuno","year":"2019","unstructured":"Yasuno, F., Nishimura, K., Negami, S., Namikawa, Y.: Development of mathematics items with dynamic objects for computer-based testing using tablet PC. Int. J. Technol. Math. Educ. 26, 131\u2013137 (2019)","journal-title":"Int. J. Technol. Math. Educ."},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1109\/TPAMI.2017.2695539","volume":"40","author":"XY Zhang","year":"2018","unstructured":"Zhang, X.Y., Yin, F., Zhang, Y.M., Liu, C.L., Bengio, Y.: Drawing and recognizing Chinese characters with recurrent neural network. IEEE Trans. Pattern Anal. Mach. Intell. 40, 849\u2013862 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR21","doi-asserted-by":"publisher","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Conference on Empirical Methods in Natural Language Processing 2014, pp. 1724\u20131734 (2014).https:\/\/doi.org\/10.3115\/v1\/d14-1179","DOI":"10.3115\/v1\/d14-1179"},{"key":"10_CR22","unstructured":"Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035 (2007)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen, C.T., Truong, T.N., Ung, H.Q., Nakagawa, M.: Online handwritten mathematical symbol segmentation and recognition with bidirectional context. In: Proceedings of International Conference on Frontiers Handwriting Recognition, pp. 355\u2013360 (2020)","DOI":"10.1109\/ICFHR2020.2020.00071"},{"key":"10_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-030-47426-3_25","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"D Ienco","year":"2020","unstructured":"Ienco, D., Interdonato, R.: Deep multivariate time series embedding clustering via attentive-gated autoencoder. In: Lauw, H.W., Wong, R.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12084, pp. 318\u2013329. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47426-3_25"},{"key":"10_CR25","unstructured":"Ma, Q., Zheng, J., Li, S., Cottrell, G.W.: Learning representations for time series clustering. In: Advances in Neural Information Processing Systems, pp. 3776\u20133786 (2019)"},{"key":"10_CR26","unstructured":"Rao, S.J., Wang, Y., Cottrell, G.: A deep siamese neural network learns the human-perceived similarity structure of facial expressions without explicit categories. In: Proceedings of the 38th Annual Conference of the Cognitive Science Society, pp. 217\u2013222 (2016)"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86331-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T22:04:46Z","timestamp":1756937086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86331-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863302","9783030863319"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86331-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 September 2021","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":"Lausanne","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iapr.org\/icdar2021","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":"340","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":"182","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":"54% - 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":"2.9","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":"4.9","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":"Additionally, 13 competition reports are included.","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)"}}]}}