{"id":"https://openalex.org/W2782695626","doi":"https://doi.org/10.1109/bigdata.2017.8258462","title":"Topic modelling enriched LSTM models for the detection of novel and emerging named entities from social media","display_name":"Topic modelling enriched LSTM models for the detection of novel and emerging named entities from social media","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782695626","doi":"https://doi.org/10.1109/bigdata.2017.8258462","mag":"2782695626"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004920162","display_name":"Patrick Jansson","orcid":null},"institutions":[{"id":"https://openalex.org/I198445264","display_name":"Arcada University of Applied Sciences","ror":"https://ror.org/02s466x84","country_code":"FI","type":"education","lineage":["https://openalex.org/I198445264"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Patrick Jansson","raw_affiliation_strings":["Arcada University of Applied Sciences, Helsinki, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arcada University of Applied Sciences, Helsinki, Finland","institution_ids":["https://openalex.org/I198445264"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109309339","display_name":"Shuhua Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I198445264","display_name":"Arcada University of Applied Sciences","ror":"https://ror.org/02s466x84","country_code":"FI","type":"education","lineage":["https://openalex.org/I198445264"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Shuhua Liu","raw_affiliation_strings":["Arcada University of Applied Sciences, Helsinki, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arcada University of Applied Sciences, Helsinki, Finland","institution_ids":["https://openalex.org/I198445264"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I198445264"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4329","last_page":"4336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8238015174865723},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.642138659954071},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6413225531578064},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6383316516876221},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6113966703414917},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6094671487808228},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5995181202888489},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5448051691055298},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5326131582260132},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5318265557289124},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5148953199386597},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5108548998832703},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.5098020434379578},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.4737095236778259},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4386172890663147},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4323580265045166},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2949712574481964},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.13282781839370728},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.12409022450447083}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8238015174865723},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.642138659954071},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6413225531578064},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6383316516876221},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6113966703414917},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6094671487808228},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5995181202888489},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5448051691055298},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5326131582260132},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5318265557289124},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5148953199386597},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5108548998832703},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5098020434379578},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4737095236778259},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4386172890663147},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4323580265045166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2949712574481964},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.13282781839370728},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.12409022450447083},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1836465849","https://openalex.org/W1940872118","https://openalex.org/W2064675550","https://openalex.org/W2134036914","https://openalex.org/W2141099517","https://openalex.org/W2143933463","https://openalex.org/W2147880316","https://openalex.org/W2151373442","https://openalex.org/W2153579005","https://openalex.org/W2153848201","https://openalex.org/W2165599843","https://openalex.org/W2170240176","https://openalex.org/W2174706414","https://openalex.org/W2250539671","https://openalex.org/W2250729567","https://openalex.org/W2296283641","https://openalex.org/W2470894770","https://openalex.org/W2478432301","https://openalex.org/W2574641775","https://openalex.org/W2588986918","https://openalex.org/W2623845346","https://openalex.org/W2757016069","https://openalex.org/W2757168963","https://openalex.org/W2757931374","https://openalex.org/W2759245808","https://openalex.org/W2760505947","https://openalex.org/W2949117887","https://openalex.org/W2949952998","https://openalex.org/W2963012544","https://openalex.org/W2963625095","https://openalex.org/W3098448896","https://openalex.org/W3100276395","https://openalex.org/W4231510805","https://openalex.org/W4294170691","https://openalex.org/W6636510571","https://openalex.org/W6639619044","https://openalex.org/W6682082992","https://openalex.org/W6682691769","https://openalex.org/W6682707525","https://openalex.org/W6684489972","https://openalex.org/W6685053522","https://openalex.org/W6685810106","https://openalex.org/W6731873861","https://openalex.org/W6733330154"],"related_works":["https://openalex.org/W3199964822","https://openalex.org/W4232132981","https://openalex.org/W4238046985","https://openalex.org/W3164948662","https://openalex.org/W3003242282","https://openalex.org/W3153597579","https://openalex.org/W4294975608","https://openalex.org/W3012824888","https://openalex.org/W4247091536","https://openalex.org/W3081652108"],"abstract_inverted_index":{"Named":[0],"entity":[1,21],"recognition":[2],"techniques":[3],"have":[4],"achieved":[5],"impressive":[6],"performance":[7,147],"on":[8,47,59,78,154,158,174,178],"well-known":[9],"datasets":[10],"of":[11,17,50,112,124,132,149],"canonical":[12],"texts.":[13],"However,":[14],"the":[15,48,60,105,130,133,165],"detection":[16,49],"emerging":[18],"and":[19,32,81,116,137,156,176],"rare":[20],"from":[22,55],"user":[23],"generated":[24],"noisy":[25],"text":[26],"such":[27],"as":[28,98],"tweets,":[29],"online":[30],"reviews":[31],"forum":[33],"discussions":[34],"still":[35],"remains":[36],"a":[37,99,117,145,161],"challenging":[38],"task.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43,65],"report":[44],"our":[45],"study":[46],"unusual,":[51],"previously":[52],"unseen":[53],"entities":[54,155,175],"social":[56],"media.":[57],"Based":[58],"WNUT2017":[61,166],"shared":[62],"task":[63],"datasets,":[64],"explore":[66],"an":[67],"approach":[68],"that":[69],"combines":[70],"LDA":[71,86],"topic":[72,87,90],"modelling":[73,88,135],"with":[74,169],"LSTM":[75,115],"deep":[76,108],"learning":[77,109],"word":[79,103],"level":[80,83,148],"character":[82],"embeddings.":[84],"The":[85,107],"generates":[89],"representation":[91],"for":[92,101],"each":[93,102],"post":[94],"which":[95],"is":[96],"used":[97],"feature":[100],"in":[104],"post.":[106],"components":[110,136],"consist":[111],"two-layer":[113],"bidirectional":[114],"CRF":[118],"output":[119],"layer.":[120],"A":[121],"large":[122],"amount":[123],"experiments":[125],"was":[126],"conducted":[127],"to":[128],"understand":[129],"effects":[131],"different":[134],"improve":[138],"system":[139],"performance.":[140],"Our":[141],"latest":[142],"results":[143],"reached":[144],"best":[146,167],"F1":[150,170],"value":[151],"at":[152,172],"45.30":[153],"43.86":[157],"surface":[159,179],"forms,":[160],"significant":[162],"improvement":[163],"over":[164],"performer":[168],"scored":[171],"41.86":[173],"40.24":[177],"forms.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
