{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T18:41:07Z","timestamp":1749926467284,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030185756"},{"type":"electronic","value":"9783030185763"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-18576-3_25","type":"book-chapter","created":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T08:05:29Z","timestamp":1556006729000},"page":"419-434","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Weighted Word Embedding Model for Text Classification"],"prefix":"10.1007","author":[{"given":"Haopeng","family":"Ren","sequence":"first","affiliation":[]},{"given":"ZeQuan","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Du","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,24]]},"reference":[{"key":"25_CR1","unstructured":"Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings (2016)"},{"key":"25_CR2","unstructured":"Blunsom, P., Grefenstette, E., Kalchbrenner, N.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (2014)"},{"key":"25_CR3","unstructured":"Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, pp. 3079\u20133087 (2015)"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Del Corso, G.M., Gulli, A., Romani, F.: Ranking a stream of news. In: Proceedings of the 14th International Conference on World Wide Web, pp. 97\u2013106. ACM (2005)","DOI":"10.1145\/1060745.1060764"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Manjunatha, V., Boyd-Graber, J., Daum\u00e9 III, H.: Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 1681\u20131691 (2015)","DOI":"10.3115\/v1\/P15-1162"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)","DOI":"10.18653\/v1\/E17-2068"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"25_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"25_CR9","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TPAMI.2008.110","volume":"31","author":"M Lan","year":"2009","unstructured":"Lan, M., Tan, C.L., Su, J., Lu, Y.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 721\u2013735 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR10","unstructured":"Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188\u20131196 (2014)"},{"key":"25_CR11","unstructured":"Li, B., Zhao, Z., Liu, T., Wang, P., Du, X.: Weighted neural bag-of-n-grams model: new baselines for text classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1591\u20131600 (2016)"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Martineau, J., Finin, T., et al.: Delta TFIDF: an improved feature space for sentiment analysis. In: ICWSM, vol. 9, p. 106 (2009)","DOI":"10.1609\/icwsm.v3i1.13979"},{"key":"25_CR13","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"25_CR14","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, vol. 10, pp. 79\u201386. Association for Computational Linguistics (2002)","DOI":"10.3115\/1118693.1118704"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., T\u00e4ckstr\u00f6m, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)","DOI":"10.18653\/v1\/D16-1244"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Shen, D., et al.: Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. arXiv preprint arXiv:1805.09843 (2018)","DOI":"10.18653\/v1\/P18-1041"},{"key":"25_CR17","unstructured":"Socher, R., Lin, C.C., Manning, C., Ng, A.Y.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 129\u2013136 (2011)"},{"issue":"1","key":"25_CR18","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1108\/eb026526","volume":"28","author":"K Sparck Jones","year":"1972","unstructured":"Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28(1), 11\u201321 (1972)","journal-title":"J. Documentation"},{"issue":"1","key":"25_CR19","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"25_CR20","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104\u20133112 (2014)"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)","DOI":"10.3115\/v1\/P15-1150"},{"key":"25_CR22","unstructured":"Wang, G., et al.: Joint embedding of words and labels for text classification. arXiv preprint arXiv:1805.04174 (2018)"},{"key":"25_CR23","unstructured":"Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pp. 90\u201394. Association for Computational Linguistics (2012)"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Wang, T., Cai, Y., Leung, H., Cai, Z., Min, H.: Entropy-based term weighting schemes for text categorization in VSM. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 325\u2013332. IEEE (2015)","DOI":"10.1109\/ICTAI.2015.57"},{"key":"25_CR25","unstructured":"Wieting, J., Bansal, M., Gimpel, K., Livescu, K.: Towards universal paraphrastic sentence embeddings. arXiv preprint arXiv:1511.08198 (2015)"},{"key":"25_CR26","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649\u2013657 (2015)"},{"key":"25_CR27","unstructured":"Zhang, Y., et al.: Adversarial feature matching for text generation. arXiv preprint arXiv:1706.03850 (2017)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-18576-3_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:06:45Z","timestamp":1710335205000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-18576-3_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030185756","9783030185763"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-18576-3_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chiang Mai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","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":"22 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2019.eng.cmu.ac.th\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"501","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":"92","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":"64","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","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":"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":"13 demo papers, 6 tutorial 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)"}}]}}