{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:42:42Z","timestamp":1742931762045,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030454388"},{"type":"electronic","value":"9783030454395"}],"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\/978-3-030-45439-5_9","type":"book-chapter","created":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T04:02:50Z","timestamp":1586577770000},"page":"126-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Patch-Based Identification of Lexical Semantic Relations"],"prefix":"10.1007","author":[{"given":"Nesrine","family":"Bannour","sequence":"first","affiliation":[]},{"given":"Ga\u00ebl","family":"Dias","sequence":"additional","affiliation":[]},{"given":"Youssef","family":"Chahir","sequence":"additional","affiliation":[]},{"given":"Houssam","family":"Akhmouch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"9_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-15712-8_1","volume-title":"Advances in Information Retrieval","author":"G Balikas","year":"2019","unstructured":"Balikas, G., Dias, G., Moraliyski, R., Akhmouch, H., Amini, M.-R.: Learning lexical-semantic relations using intuitive cognitive links. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) ECIR 2019. LNCS, vol. 11437, pp. 3\u201318. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-15712-8_1"},{"key":"9_CR2","unstructured":"Baroni, M., Bernardi, R., Do, N.Q., Shan, C.C.: Entailment above the word level in distributional semantics. In: 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 23\u201332 (2012)"},{"key":"9_CR3","unstructured":"Baroni, M., Lenci, A.: How we Blessed distributional semantic evaluation. In: Workshop on Geometrical Models of Natural Language Semantics (GEMS) associated to Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 1\u201310 (2011)"},{"key":"9_CR4","first-page":"61","volume":"13","author":"A Blank","year":"1999","unstructured":"Blank, A.: Why do new meanings occur? A cognitive typology of the motivations for lexical semantic change. Cogn. Linguist. Res. 13, 61\u201390 (1999)","journal-title":"Cogn. Linguist. Res."},{"key":"9_CR5","unstructured":"Chollet, F.: Keras. https:\/\/keras.io (2015)"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Dong, L., Mallinson, J., Reddy, S., Lapata, M.: Learning to paraphrase for question answering. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 875\u2013886 (2017)","DOI":"10.18653\/v1\/D17-1091"},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1109\/TASLP.2014.2377580","volume":"23","author":"R Fu","year":"2015","unstructured":"Fu, R., Guo, J., Zhao, Y., Che, W., Wang, H., Liu, T.: Learning semantic hierarchies: a continuous vector space approach. IEEE\/ACM Trans. Audio Speech Lang. Process. 23, 461\u2013471 (2015)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-016-9475-9","volume":"47","author":"M Gambhir","year":"2016","unstructured":"Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1\u201366 (2016). https:\/\/doi.org\/10.1007\/s10462-016-9475-9","journal-title":"Artif. Intell. Rev."},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Glavas, G., Vulic, I.: Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment. In: 57th Conference of the Association for Computational Linguistics (ACL), pp. 4824\u20134830 (2019)","DOI":"10.18653\/v1\/P19-1476"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Gupta, A., Lebret, R., Harkous, H., Aberer, K.: Taxonomy induction using hypernym subsequences. In: Conference on Information and Knowledge Management (CIKM), pp. 1329\u20131338 (2017)","DOI":"10.1145\/3132847.3133041"},{"issue":"2\u20133","key":"9_CR11","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","volume":"10","author":"ZS Harris","year":"1954","unstructured":"Harris, Z.S.: Distributional structure. Word 10(2\u20133), 146\u2013162 (1954)","journal-title":"Word"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: 14th Conference on Computational Linguistics (COLING), pp. 539\u2013545 (1992)","DOI":"10.3115\/992133.992154"},{"key":"9_CR13","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198270126.001.0001","volume-title":"Foundations of Language: Brain, Meaning, Grammar, and Evolution","author":"R Jackendoff","year":"2002","unstructured":"Jackendoff, R.: Foundations of Language: Brain, Meaning, Grammar, and Evolution. Oxford University Press, Oxford (2002)"},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MCI.2019.2901085","volume":"14","author":"S Jim\u00e9nez","year":"2019","unstructured":"Jim\u00e9nez, S., Gonz\u00e1lez, F.A., Gelbukh, A.F., Due\u00f1as, G.: Word2set: WordNet-based word representation rivaling neural word embedding for lexical similarity and sentiment analysis. IEEE Comput. Intell. Mag. 14, 41\u201353 (2019)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"9_CR15","first-page":"17","volume":"168","author":"N Kathuria","year":"2017","unstructured":"Kathuria, N., Mittal, K.: A comprehensive survey on query expansion techniques, their issues and challenges. Int. J. Comput. Appl. 168, 17\u201320 (2017)","journal-title":"Int. J. Comput. Appl."},{"key":"9_CR16","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)"},{"key":"9_CR17","unstructured":"Kozareva, Z., Hovy, E.: A semi-supervised method to learn and construct taxonomies using the web. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1110\u20131118 (2010)"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Ledig, C., Shi, W., Bai, W., Rueckert, D.: Patch-based evaluation of image segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3065\u20133072 (2014)","DOI":"10.1109\/CVPR.2014.392"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Levy, O., Remus, S., Biemann, C., Dagan, I.: Do supervised distributional methods really learn lexical inference relations? In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL), pp. 970\u2013976 (2015)","DOI":"10.3115\/v1\/N15-1098"},{"key":"9_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/978-3-319-25903-1_5","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"O L\u00e9zoray","year":"2015","unstructured":"L\u00e9zoray, O.: Patch-Based mathematical morphology for image processing, segmentation and classification. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 46\u201357. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25903-1_5"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. In: 55th Annual Meeting of the Association for Computational Linguistics (ACL) (2017)","DOI":"10.18653\/v1\/P17-1001"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Lu, P., Ji, L., Zhang, W., Duan, N., Zhou, M., Wang, J.: R-VQA: learning visual relation facts with semantic attention for visual question answering. In: 24th International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1880\u20131889 (2018)","DOI":"10.1145\/3219819.3220036"},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1515\/plc-2015-0001","volume":"19","author":"N MIko\u0142ajczak-Matyja","year":"2015","unstructured":"MIko\u0142ajczak-Matyja, N.: The associative structure of the mental lexicon: hierarchical semantic relations in the minds of blind and sighted language users. Psychol. Lang. Commun. 19, 1\u201318 (2015)","journal-title":"Psychol. Lang. Commun."},{"issue":"4","key":"9_CR24","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/ijl\/3.4.235","volume":"3","author":"GA Miller","year":"1990","unstructured":"Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235\u2013244 (1990)","journal-title":"Int. J. Lexicogr."},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Nguyen, K.A., Schulte im Walde, S., Vu, N.T.: Distinguishing antonyms and synonyms in a pattern-based neural network. In: 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 76\u201385 (2017)","DOI":"10.18653\/v1\/E17-1008"},{"key":"9_CR26","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report 1999\u201366, Stanford InfoLab, November 1999"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"9_CR28","unstructured":"Roller, S., Erk, K., Boleda, G.: Inclusive yet selective: supervised distributional hypernymy detection. In: 25th International Conference on Computational Linguistics (COLING), pp. 1025\u20131036 (2014)"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Roller, S., Kiela, D., Nickel, M.: Hearst patterns revisited: automatic hypernym detection from large text corpora. In: 56th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 358\u2013363 (2018)","DOI":"10.18653\/v1\/P18-2057"},{"key":"9_CR30","unstructured":"Santus, E., Lenci, A., Chiu, T., Lu, Q., Huang, C.: Nine features in a random forest to learn taxonomical semantic relations. In: 10th International Conference on Language Resources and Evaluation (LREC), pp. 4557\u20134564 (2016)"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Santus, E., Shwartz, V., Schlechtweg, D.: Hypernyms under siege: linguistically-motivated artillery for hypernymy detection. In: 15th Conference of the European Chapter of the Association for Computational Linguistics, pp. 65\u201375 (2017)","DOI":"10.18653\/v1\/E17-1007"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Shwartz, V., Goldberg, Y., Dagan, I.: Improving hypernymy detection with an integrated path-based and distributional method. In: 54th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 2389\u20132398 (2016)","DOI":"10.18653\/v1\/P16-1226"},{"key":"9_CR33","unstructured":"Snow, R., Jurafsky, D., Ng, A.Y.: Learning syntactic patterns for automatic hypernym discovery. In: 17th International Conference on Neural Information Processing Systems (NIPS), pp. 1297\u20131304 (2004)"},{"key":"9_CR34","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Vulic, I., Mrksic, N.: Specialising word vectors for lexical entailment. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 1134\u20131145 (2018)","DOI":"10.18653\/v1\/N18-1103"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Vulic, I., Mrksic, N., Reichart, R., S\u00e9aghdha, D.\u00d3., Young, S.J., Korhonen, A.: Morph-fitting: fine-tuning word vector spaces with simple language-specific rules. In: 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 56\u201368 (2017)","DOI":"10.18653\/v1\/P17-1006"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Vylomova, E., Rimell, L., Cohn, T., Baldwin, T.: Take and took, gaggle and goose, book and read: evaluating the utility of vector differences for lexical relation learning. In: 54th Annual Meeting of the Association for Computational Linguistics, pp. 1671\u20131682 (2016)","DOI":"10.18653\/v1\/P16-1158"},{"key":"9_CR38","first-page":"1","volume":"3","author":"Y Wang","year":"2009","unstructured":"Wang, Y.: On cognitive foundations of creativity and the cognitive process of creation. Int. J. Cogn. Inform. Nat. Intell. 3, 1\u201318 (2009)","journal-title":"Int. J. Cogn. Inform. Nat. Intell."},{"key":"9_CR39","unstructured":"Weeds, J., Clarke, D., Reffin, J., Weir, D.J., Keller, B.: Learning to distinguish hypernyms and co-hyponyms. In: 5th International Conference on Computational Linguistics (COLING), pp. 2249\u20132259 (2014)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45439-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:12:30Z","timestamp":1710357150000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45439-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030454388","9783030454395"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45439-5_9","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":"8 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2020.org\/","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":"457","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":"55","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":"46","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":"12% - 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":"4","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":"Also included: 8 reproducibility papers, 10 demonstration papers, 12 CLEF organizers lab track papers, 7 doctoral consortium papers, 4 workshops, 3 tutorials. Due to the COVID-19 pandemic, this conference was held virtually.","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"}]}}