socialmediaie/SocialMediaIE-MetaCorpus-v1

SocialMediaIE - Social Media Information Extraction

List of datasets used for training SocialMediaIE

• Dataset referencs
  • Tagging datasets
• Dataset statistics
  • Sentiment
  • Abusive
  • Uncertainity
  • Part of Speech Tagging
  • Named Entity Recognition
  • Chunking
  • Supersense Tagging
• Dataset references

Table of contents generated with markdown-toc

Dataset referencs

Tagging datasets

• POS tagging: [17,18] (OW), [7] (TIE), [20] (RT), 15, [22] (DS), [12] (FS), and [12,13] (LW).
• NER: [20] (RT), [23] (W16), [6] (W17), [9] (FN), [10] (HG),and [4] (BR), [24] (MM), [11] (YD), [21] (we do not evaluate on this) and [1] (MSM).
• Chunking: [20] (RT) dataset.
• Supersense tagging: [20] (RT) dataset, the [14] (JH) dataset.

Dataset statistics

Sentiment

		tokens	tweets	vocab
data	split
Airline	dev	20079	981	3273
	test	50777	2452	5630
	train	182040	8825	11697
Clarin	dev	80672	4934	15387
	test	205126	12334	31373
	train	732743	44399	84279
GOP	dev	16339	803	3610
	test	41226	2006	6541
	train	148358	7221	14342
Healthcare	dev	15797	724	3304
	test	16022	717	3471
	train	14923	690	3511
Obama	dev	3472	209	1118
	test	8816	522	2043
	train	31074	1877	4349
SemEval	dev	105108	4583	14468
	test	528234	23103	43812
	train	281468	12245	29673

Abusive

		tokens	tweets	vocab
data	split
Founta	dev	102534	4663	22529
	test	256569	11657	44540
	train	922028	41961	118349
WaseemSRW	dev	25588	1464	5907
	test	64893	3659	10646
	train	234550	13172	23042

Uncertainity

		tokens	tweets	vocab
data	split
Riloff	dev	2126	145	1002
	test	5576	362	1986
	train	19652	1301	5090
Swamy	dev	1597	73	738
	test	3909	183	1259
	train	14026	655	2921

Part of Speech Tagging

		labels	labels_unique	sequences	tokens_unique	total_tokens
data_key	split_prefix
Owoputi	train	[!, #, $, &, ,, @, A, D, E, G, L, M, N, O, P, R, S, T, U, V, X, Y, Z, ^, ~]	25	1547	6572	22326
	dev	[!, #, $, &, ,, @, A, D, E, G, L, N, O, P, R, S, T, U, V, X, Z, ^, ~]	23	327	2036	4823
	test	[!, #, $, &, ,, @, A, D, E, G, L, N, O, P, R, S, T, U, V, X, Z, ^, ~]	23	500	2754	7152
Foster	test	[ADJ, ADP, ADV, CCONJ, DET, NOUN, NUM, PART, PRON, PUNCT, VERB, X]	12	250	1068	2841
TwitIE	dev	['', (, ), ,, :, CC, CD, DT, FW, HT, IN, JJ, JJR, JJS, MD, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP$, PUNCT, RB, RBR, RBS, RP, RT, SYM, TO, UH, URL, USR, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WRB]	43	269	1229	2998
	test	['', (, ), ,, :, CC, CD, DT, EX, FW, HT, IN, JJ, JJR, JJS, MD, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP#, PUNCT, RB, RBR, RBS, RP, RT, SYM, TO, UH, URL, USR, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WRB]	45	632	3539	12196
Ritter	dev	['', (, ), ,, :, CC, CD, DT, HT, IN, JJ, JJR, JJS, MD, NN, NNP, NNS, POS, PRP, PRP$, PUNCT, RB, RBR, RP, RT, TO, UH, URL, USR, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WRB]	38	71	695	1362
	test	['', (, ), ,, :, CC, CD, DT, EX, HT, IN, JJ, JJR, JJS, MD, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP$, PUNCT, RB, RBR, RP, RT, SYM, TO, UH, URL, USR, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WRB]	41	84	735	1627
lowlands	dev	[ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X]	17	710	3271	11759
	test	[ADJ, ADP, ADV, CCONJ, DET, NOUN, NUM, PART, PRON, PUNCT, VERB, X]	12	1318	4805	19794
Tweetbankv2	dev	[ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X]	17	710	3271	11759
	train	[ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X]	17	1639	5632	24753
	test	[ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X]	17	1201	4699	19095
DiMSUM2016	train	[ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X]	17	4799	9113	73826
	test	[ADJ, ADP, ADV, AUX, CCONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SCONJ, SYM, VERB, X]	17	1000	4010	16500

Named Entity Recognition

		boundaries	labels	labels_unique	sequences	tokens_unique	total_tokens
data_key	split_prefix
Finin	train	[I, B, O]	[LOC, PER, ORG]	3	10000	19663	172188
	test	[I, B, O]	[LOC, PER, ORG]	3	5369	13027	97525
Hege	test	[I, B, O]	[LOC, PER, ORG]	3	1545	4552	20664
Ritter	train	[I, B, O]	[COMPANY, OTHER, FACILITY, PERSON, MOVIE, MUSICARTIST, GEO-LOC, TVSHOW, PRODUCT, SPORTSTEAM]	10	1900	7695	36936
	dev	[I, B, O]	[COMPANY, OTHER, PERSON, FACILITY, MOVIE, MUSICARTIST, GEO-LOC, TVSHOW, PRODUCT, SPORTSTEAM]	10	240	1731	4612
	test	[I, B, O]	[COMPANY, OTHER, PERSON, FACILITY, MOVIE, MUSICARTIST, GEO-LOC, TVSHOW, PRODUCT, SPORTSTEAM]	10	254	1776	4921
YODIE	train	[I, B, O]	[COMPANY, OTHER, PERSON, LOCATION, FACILITY, MOVIE, MUSICARTIST, GEO-LOC, UNK, TVSHOW, PRODUCT, SPORTSTEAM, ORGANIZATION]	13	396	2554	7905
	test	[I, B, O]	[COMPANY, OTHER, FACILITY, LOCATION, PERSON, MOVIE, MUSICARTIST, GEO-LOC, UNK, TVSHOW, PRODUCT, SPORTSTEAM, ORGANIZATION]	13	397	2578	8032
WNUT2016	train	[I, B, O]	[COMPANY, OTHER, FACILITY, PERSON, MOVIE, MUSICARTIST, GEO-LOC, TVSHOW, PRODUCT, SPORTSTEAM]	10	2394	9068	46469
	test	[I, B, O]	[COMPANY, OTHER, PERSON, FACILITY, MOVIE, MUSICARTIST, GEO-LOC, TVSHOW, PRODUCT, SPORTSTEAM]	10	3850	16012	61908
	dev	[I, B, O]	[COMPANY, OTHER, FACILITY, PERSON, MOVIE, MUSICARTIST, GEO-LOC, TVSHOW, PRODUCT, SPORTSTEAM]	10	1000	5563	16261
WNUT2017	train	[I, B, O]	[GROUP, CORPORATION, PERSON, LOCATION, PRODUCT, CREATIVE-WORK]	6	3394	12840	62730
	dev	[I, B, O]	[GROUP, CORPORATION, PERSON, LOCATION, PRODUCT, CREATIVE-WORK]	6	1009	3538	15733
	test	[I, B, O]	[GROUP, CORPORATION, PERSON, LOCATION, PRODUCT, CREATIVE-WORK]	6	1287	5759	23394
MSM2013	train	[I, B, O]	[LOC, MISC, PER, ORG]	4	2815	8514	51521
	test	[I, B, O]	[LOC, PER, ORG, MISC]	4	1450	5701	29089
NEEL2016	train	[I, B, O]	[PERSON, THING, LOCATION, EVENT, PRODUCT, ORGANIZATION, CHARACTER]	7	2588	9731	51669
	dev	[I, B, O]	[PERSON, LOCATION, THING, EVENT, PRODUCT, ORGANIZATION, CHARACTER]	7	88	762	1647
	test	[I, B, O]	[PERSON, THING, LOCATION, EVENT, PRODUCT, ORGANIZATION, CHARACTER]	7	2663	9894	47488
BROAD	train	[I, B, O]	[LOC, PER, ORG]	3	5605	19523	90060
	dev	[I, B, O]	[LOC, PER, ORG]	3	933	5312	15169
	test	[I, B, O]	[LOC, PER, ORG]	3	2802	11772	45159
MultiModal	train	[I, B, O]	[LOC, PER, ORG, MISC]	4	4000	20221	64439
	dev	[I, B, O]	[LOC, MISC, PER, ORG]	4	1000	6832	16178
	test	[I, B, O]	[LOC, PER, ORG, MISC]	4	3257	17381	52822

Chunking

		boundaries	labels	labels_unique	sequences	tokens_unique	total_tokens
data_key	split_prefix
Ritter	train	[I, B, O]	[ADJP, PP, INTJ, ADVP, PRT, NP, SBAR, VP, CONJP]	9	551	3158	10584
	dev	[I, B, O]	[ADJP, PP, INTJ, ADVP, PRT, NP, SBAR, VP]	8	118	994	2317
	test	[I, B, O]	[ADJP, PP, INTJ, ADVP, PRT, NP, SBAR, VP]	8	119	988	2310

Supersense Tagging

		boundaries	labels	labels_unique	sequences	tokens_unique	total_tokens
data_key	split_prefix
Ritter	train	[I, B, O]	[NOUN.BODY, NOUN.STATE, NOUN.ARTIFACT, NOUN.ATTRIBUTE, NOUN.FOOD, NOUN.TOPS, NOUN.COGNITION, NOUN.EVENT, NOUN.OBJECT, NOUN.MOTIVE, NOUN.GROUP, VERB.COMMUNICATION, NOUN.PHENOMENON, VERB.POSSESSION, VERB.COMPETITION, NOUN.POSSESSION, NOUN.FEELING, VERB.SOCIAL, NOUN.ANIMAL, VERB.CREATION, VERB.CONSUMPTION, VERB.PERCEPTION, VERB.CONTACT, VERB.WEATHER, VERB.BODY, NOUN.LOCATION, NOUN.QUANTITY, NOUN.SUBSTANCE, NOUN.RELATION, NOUN.TIME, NOUN.PERSON, VERB.COGNITION, VERB.EMOTION, NOUN.PLANT, VERB.STATIVE, VERB.MOTION, NOUN.COMMUNICATION, NOUN.PROCESS, NOUN.ACT, VERB.CHANGE]	40	551	3174	10652
	dev	[I, B, O]	[NOUN.BODY, NOUN.STATE, NOUN.ARTIFACT, NOUN.ATTRIBUTE, NOUN.FOOD, NOUN.COGNITION, NOUN.EVENT, NOUN.OBJECT, NOUN.MOTIVE, NOUN.GROUP, VERB.COMMUNICATION, NOUN.PHENOMENON, VERB.COMPETITION, VERB.POSSESSION, NOUN.POSSESSION, NOUN.FEELING, VERB.SOCIAL, NOUN.ANIMAL, VERB.CREATION, VERB.CONSUMPTION, VERB.PERCEPTION, VERB.CONTACT, VERB.BODY, NOUN.LOCATION, NOUN.QUANTITY, NOUN.SUBSTANCE, NOUN.RELATION, NOUN.TIME, VERB.COGNITION, NOUN.PERSON, VERB.EMOTION, NOUN.PLANT, VERB.STATIVE, VERB.MOTION, NOUN.COMMUNICATION, NOUN.ACT, VERB.CHANGE]	37	118	1014	2242
	test	[I, B, O]	[NOUN.BODY, NOUN.STATE, NOUN.ARTIFACT, NOUN.ATTRIBUTE, NOUN.FOOD, NOUN.TOPS, NOUN.COGNITION, NOUN.EVENT, NOUN.OBJECT, NOUN.MOTIVE, NOUN.SHAPE, NOUN.GROUP, VERB.COMMUNICATION, NOUN.PHENOMENON, VERB.POSSESSION, NOUN.FEELING, NOUN.POSSESSION, VERB.COMPETITION, VERB.SOCIAL, NOUN.ANIMAL, VERB.CREATION, VERB.CONSUMPTION, VERB.PERCEPTION, VERB.CONTACT, VERB.WEATHER, VERB.BODY, NOUN.LOCATION, NOUN.QUANTITY, NOUN.SUBSTANCE, NOUN.RELATION, NOUN.TIME, NOUN.PERSON, VERB.COGNITION, VERB.EMOTION, VERB.STATIVE, VERB.MOTION, NOUN.COMMUNICATION, NOUN.PROCESS, NOUN.ACT, VERB.CHANGE]	40	118	1011	2291
Johannsen2014	test	[I, B, O]	[NOUN.BODY, NOUN.STATE, NOUN.ARTIFACT, NOUN.ATTRIBUTE, NOUN.FOOD, NOUN.COGNITION, NOUN.EVENT, NOUN.OBJECT, NOUN.SHAPE, NOUN.GROUP, VERB.COMMUNICATION, NOUN.PHENOMENON, VERB.COMPETITION, VERB.POSSESSION, NOUN.FEELING, NOUN.POSSESSION, VERB.SOCIAL, NOUN.ANIMAL, VERB.CREATION, VERB.CONSUMPTION, VERB.PERCEPTION, VERB.CONTACT, VERB.BODY, NOUN.LOCATION, NOUN.QUANTITY, NOUN.SUBSTANCE, NOUN.RELATION, NOUN.TIME, NOUN.PERSON, VERB.COGNITION, VERB.EMOTION, VERB.STATIVE, VERB.MOTION, NOUN.COMMUNICATION, NOUN.PROCESS, NOUN.ACT, VERB.CHANGE]	37	200	1249	3064

Dataset references

• [1] Amparo Elizabeth Cano, Andrea Varga, Matthew Rowe, Milan Stankovic, and Aba-Sah Dadzie. 2013. Making Sense of Microposts (#MSM2013) Concept ExtractionChallenge. In#MSM.
• [2] Richard A. Caruana. 1993. Multitask Learning: A Knowledge-Based Source ofInductive Bias. InMachine Learning Proceedings 1993. Elsevier, 41–48. https://doi.org/10.1016/b978-1-55860-307-3.50012-5
• [3] Ronan Collbert, Jason Weston, LÃľon Bottou, Michael Karlen, Koray Kavukcuoglu,and Pavel Kuksa. 2011. Natural Language Processing (Almost) from Scratch.Journal ofMachine Learning Research12 (2 2011), 2493–2537. http://dl.acm.org/citation.cfm?id=2078186
• [4] Leon Derczynski, Kalina Bontcheva, and Ian Roberts. 2016.Broad Twit-ter Corpus: A Diverse Named Entity Recognition Resource.Proceedings ofCOLING 2016, the 26th International Conference on Computational Linguis-tics: Technical Papers(2016), 1169–1179.http://aclanthology.info/papers/broad-twitter-corpus-a-diverse-named-entity-recognition-resource
• [5] Leon Derczynski, Diana Maynard, Niraj Aswani, and Kalina Bontcheva. 2013.Microblog-genre Noise and Impact on Semantic Annotation Accuracy. InPro-ceedings of the 24th ACM Conference on Hypertext and Social Media (HT ’13). ACM,New York, NY, USA, 21–30. https://doi.org/10.1145/2481492.2481495
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• [7] Leon Derczynski, Alan Ritter, Sam Clark, and Kalina Bontcheva. 2013. Twit-ter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data.Pro-ceedings of the International Conference Recent Advances in Natural LanguageProcessing RANLP 2013(2013), 198–206.http://aclanthology.info/papers/twitter-part-of-speech-tagging-for-all-overcoming-sparse-and-noisy-data
• [8] Jacob Eisenstein. 2013. What to do about bad language on the internet. InProceedings of the 2013 Conference of the North American Chapter of the Associationfor Computational Linguistics: Human Language Technologies. Association forComputational Linguistics, Atlanta, Georgia, 359–369. https://www.aclweb.org/anthology/N13-1037
• [9] Tim Finin, William Murnane, Anand Karandikar, Nicholas Keller, Justin Mar-tineau, and Mark Dredze. 2010. Annotating Named Entities in Twitter Data withCrowdsourcing.Proceedings of the NAACL HLT 2010 Workshop on Creating Speechand Language Data with Amazon’s Mechanical Turk2010, January, 80–88.
• [10] Hege Fromreide, Dirk Hovy, and Anders Søgaard. 2014. Crowdsourcing and anno-tating NER for Twitter #drift. InProceedings of the Ninth International Conferenceon Language Resources and Evaluation (LREC’14). European language resourcesdistribution agency, 2544–2547. http://www.lrec-conf.org/proceedings/lrec2014/pdf/421_Paper.pdf
• [11] Genevieve Gorrell, Johann Petrak, and Kalina Bontcheva. 2015. Using @TwitterConventions to Improve #LOD-Based Named Entity Disambiguation. Springer,Cham, 171–186. https://doi.org/10.1007/978-3-319-18818-8{_}11
• [12] Dirk Hovy, Barbara Plank, and Anders Søgaard. 2014. Experiments with crowd-sourced re-annotation of a POS tagging data set. InProceedings of the 52ndAnnual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, 377–382.https://doi.org/10.3115/v1/P14-2062
• [13] Dirk Hovy, Barbara Plank, and Anders Søgaard. 2014. When POS data setsdon’t add up: Combatting sample bias.Proceedings of the Ninth InternationalConference on Language Resources and Evaluation (LREC-2014)(2014). https://aclanthology.coli.uni-saarland.de/papers/L14-1402/l14-1402
• [14] Anders Johannsen, Dirk Hovy, HÃľctor Martínez Alonso, Barbara Plank, andAnders Søgaard. 2014. More or less supervised supersense tagging of Twitter.InProceedings of the Third Joint Conference on Lexical and Computational Se-mantics (*SEM 2014). Association for Computational Linguistics and Dublin CityUniversity, Stroudsburg, PA, USA, 1–11. https://doi.org/10.3115/v1/S14-1001
• [15] Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, and Noah A. Smith.2018. Parsing Tweets into Universal Dependencies. InProceedings of the 2018Conference of the North American Chapter of the Association for ComputationalLinguistics: Human Language Technologies, Volume 1 (Long Papers). Associationfor Computational Linguistics, New Orleans, Louisiana, 965–975. https://doi.org/10.18653/v1/N18-1088
• [16] Héctor Martínez Alonso and Barbara Plank. 2017. When is multitask learningeffective? Semantic sequence prediction under varying data conditions. InPro-ceedings of the 15th Conference of the European Chapter of the Association forComputational Linguistics: Volume 1, Long Papers. Association for ComputationalLinguistics, Valencia, Spain, 44–53. https://www.aclweb.org/anthology/E17-1005
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• [18] Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, Nathan Schnei-der, and Noah a Smith. 2013. Improved Part-of-Speech Tagging for OnlineConversational Text with Word Clusters.Proceedings of NAACL-HLT 2013June(2013), 380–390. https://doi.org/10.1.1.343.3572
• [19] Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark,Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Repre-sentations. InProceedings of the 2018 Conference of the North American Chapterof the Association for Computational Linguistics: Human Language Technologies,Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans,Louisiana, 2227–2237. https://doi.org/10.18653/v1/N18-1202
• [20] Alan Ritter, Sam Clark, and Oren Etzioni. 2011. Named entity recognition intweets: an experimental study. InProceedings of Emperical Methods for NaturalLangauge Processing. 1524–1534. https://doi.org/10.1075/li.30.1.03nad
• [21] Giuseppe Rizzo, Marieke van Erp, Julien Plu, and RaphaÃńl Troncy. 2016. MakingSense of Microposts (#Microposts2016) Named Entity rEcognition and Linking(NEEL) Challenge. InWorkshop on Making Sense of Microposts (#Microposts2016).Montréal. http://ceur-ws.org/Vol-1691/microposts2016_neel-challenge-report/http://ceur-ws.org/Vol-1691/microposts2016_neel-challenge-report/microposts2016_neel-challenge-report.pdfhttp://microposts2016.seas.upenn.edu/challenge.htmlhttp://ceur-ws.org/Vol-1691/mic
• [22] Nathan Schneider and Noah A. Smith. 2015. A Corpus and Model IntegratingMultiword Expressions and Supersenses. InProceedings of the 2015 Conference ofthe North American Chapter of the Association for Computational Linguistics: Hu-man Language Technologies. Association for Computational Linguistics, Denver,Colorado, 1537–1547. https://doi.org/10.3115/v1/N15-1177
• [23] Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marn-effe, and Wei Xu. 2016.Results of the WNUT16 Named Entity Recog-nition Shared Task.Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)(2016), 138–144.http://aclanthology.info/papers/results-of-the-wnut16-named-entity-recognition-shared-task
• [24] Qi Zhang, Jinlan Fu, Xiaoyu Liu, and Xuanjing Huang. 2018. Adaptive Co-attention Network for Named Entity Recognition in Tweets. https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16432