{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:07:31Z","timestamp":1730200051139,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1109\/bigdata.2017.8258430","type":"proceedings-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T17:47:28Z","timestamp":1516038448000},"page":"4111-4116","source":"Crossref","is-referenced-by-count":4,"title":["City event detection from social media with neural embeddings and topic model visualization"],"prefix":"10.1109","author":[{"given":"Shuhua","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Jansson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1104","article-title":"Open domain event extraction from twitter","author":"mausam","year":"2012","journal-title":"Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741083"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772777"},{"key":"ref13","article-title":"Feasibility study on detection of transportation information exploiting Twitter as a sensor","author":"sasaki","year":"2012","journal-title":"AAAI Technical Report WS-12–04 Association for the Advancement of Artificial Intelligence"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-3110"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.09.069"},{"key":"ref16","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"0","journal-title":"ICLR 2013 Workshop Proceedings"},{"key":"ref17","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"2013","journal-title":"Proceedings of NIPS"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref19","first-page":"135","article-title":"Enriching word vectors with subword Information","volume":"5","author":"bojanowski","year":"2017","journal-title":"Trans AC"},{"key":"ref4","first-page":"601","article-title":"Latent Di-richlet Allocation","author":"blei","year":"2003","journal-title":"Advances in neural information processing systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-41338-4_1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0421-2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2133806.2133826"},{"key":"ref8","first-page":"23","article-title":"Towards accurate avent detection in social media: a weakly supervised approach for learning implicit event indicators","author":"jain","year":"2016","journal-title":"Proc Workshop Noisy User-generated Text"},{"key":"ref7","first-page":"856","article-title":"Online learning for Latent Dirichlet Allocation","volume":"23","author":"hoffman","year":"2010","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12017"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2717317"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1214"},{"key":"ref20","first-page":"427","article-title":"Bag of tricks for efficient text classification","author":"grave","year":"2017","journal-title":"EACL"},{"key":"ref22","article-title":"Exploiting neural embeddings for social media data analysis","author":"hasan","year":"2015","journal-title":"TREC"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914762"},{"key":"ref24","article-title":"Towards universal paraphrastic sentence embeddings","author":"wieting","year":"2016","journal-title":"Proceedings of International Conference on Learning Representations"},{"key":"ref23","article-title":"A simple but tough-to-beat baseline for sentence embeddings","author":"arora","year":"2017","journal-title":"Proceedinsg of ICLR"}],"event":{"name":"2017 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2017,12,11]]},"location":"Boston, MA","end":{"date-parts":[[2017,12,14]]}},"container-title":["2017 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8241556\/8257893\/08258430.pdf?arnumber=8258430","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,2,19]],"date-time":"2018-02-19T18:58:14Z","timestamp":1519066694000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8258430\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/bigdata.2017.8258430","relation":{},"subject":[],"published":{"date-parts":[[2017,12]]}}}