--- language: - en license: apache-2.0 configs: - config_name: abusive-founta data_files: - path: data/ABUSIVE/Founta/test.json split: test - path: data/ABUSIVE/Founta/train.json split: train - path: data/ABUSIVE/Founta/validation.json split: validation - config_name: abusive-waseemsrw data_files: - path: data/ABUSIVE/WaseemSRW/test.json split: test - path: data/ABUSIVE/WaseemSRW/train.json split: train - path: data/ABUSIVE/WaseemSRW/validation.json split: validation - config_name: chunking-ritter data_files: - path: data/CHUNKING/Ritter/test.json split: test - path: data/CHUNKING/Ritter/train.json split: train - path: data/CHUNKING/Ritter/validation.json split: validation - config_name: ner-broad data_files: - path: data/NER/BROAD/test.json split: test - path: data/NER/BROAD/train.json split: train - path: data/NER/BROAD/validation.json split: validation - config_name: ner-finin data_files: - path: data/NER/Finin/test.json split: test - path: data/NER/Finin/train.json split: train - config_name: ner-hege data_files: - path: data/NER/Hege/test.json split: test - config_name: ner-msm2013 data_files: - path: data/NER/MSM2013/test.json split: test - path: data/NER/MSM2013/train.json split: train - config_name: ner-multimodal data_files: - path: data/NER/MultiModal/test.json split: test - path: data/NER/MultiModal/train.json split: train - path: data/NER/MultiModal/validation.json split: validation - config_name: ner-neel2016 data_files: - path: data/NER/NEEL2016/test.json split: test - path: data/NER/NEEL2016/train.json split: train - path: data/NER/NEEL2016/validation.json split: validation - config_name: ner-ritter data_files: - path: data/NER/Ritter/test.json split: test - path: data/NER/Ritter/train.json split: train - path: data/NER/Ritter/validation.json split: validation - config_name: ner-wnut2016 data_files: - path: data/NER/WNUT2016/test.json split: test - path: data/NER/WNUT2016/train.json split: train - path: data/NER/WNUT2016/validation.json split: validation - config_name: ner-wnut2017 data_files: - path: data/NER/WNUT2017/test.json split: test - path: data/NER/WNUT2017/train.json split: train - path: data/NER/WNUT2017/validation.json split: validation - config_name: ner-yodie data_files: - path: data/NER/YODIE/test.json split: test - path: data/NER/YODIE/train.json split: train - config_name: pos-dimsum2016 data_files: - path: data/POS/DiMSUM2016/test.json split: test - path: data/POS/DiMSUM2016/train.json split: train - config_name: pos-foster data_files: - path: data/POS/Foster/test.json split: test - config_name: pos-lowlands data_files: - path: data/POS/lowlands/test.json split: test - config_name: pos-owoputi data_files: - path: data/POS/Owoputi/test.json split: test - path: data/POS/Owoputi/train.json split: train - path: data/POS/Owoputi/validation.json split: validation - config_name: pos-ritter data_files: - path: data/POS/Ritter/test.json split: test - path: data/POS/Ritter/train.json split: train - path: data/POS/Ritter/validation.json split: validation - config_name: pos-tweetbankv2 data_files: - path: data/POS/Tweetbankv2/test.json split: test - path: data/POS/Tweetbankv2/train.json split: train - path: data/POS/Tweetbankv2/validation.json split: validation - config_name: pos-twitie data_files: - path: data/POS/TwitIE/test.json split: test - path: data/POS/TwitIE/validation.json split: validation - config_name: sentiment-airline data_files: - path: data/SENTIMENT/Airline/test.json split: test - path: data/SENTIMENT/Airline/train.json split: train - path: data/SENTIMENT/Airline/validation.json split: validation - config_name: sentiment-clarin data_files: - path: data/SENTIMENT/Clarin/test.json split: test - path: data/SENTIMENT/Clarin/train.json split: train - path: data/SENTIMENT/Clarin/validation.json split: validation - config_name: sentiment-gop data_files: - path: data/SENTIMENT/GOP/test.json split: test - path: data/SENTIMENT/GOP/train.json split: train - path: data/SENTIMENT/GOP/validation.json split: validation - config_name: sentiment-healthcare data_files: - path: data/SENTIMENT/Healthcare/test.json split: test - path: data/SENTIMENT/Healthcare/train.json split: train - path: data/SENTIMENT/Healthcare/validation.json split: validation - config_name: sentiment-obama data_files: - path: data/SENTIMENT/Obama/test.json split: test - path: data/SENTIMENT/Obama/train.json split: train - path: data/SENTIMENT/Obama/validation.json split: validation - config_name: sentiment-semeval data_files: - path: data/SENTIMENT/SemEval/test.json split: test - path: data/SENTIMENT/SemEval/train.json split: train - path: data/SENTIMENT/SemEval/validation.json split: validation - config_name: supersense-johannsen2014 data_files: - path: data/SUPERSENSE/Johannsen2014/test.json split: test - config_name: supersense-ritter data_files: - path: data/SUPERSENSE/Ritter/test.json split: test - path: data/SUPERSENSE/Ritter/train.json split: train - path: data/SUPERSENSE/Ritter/validation.json split: validation - config_name: uncertainity-riloff data_files: - path: data/UNCERTAINITY/Riloff/test.json split: test - path: data/UNCERTAINITY/Riloff/train.json split: train - path: data/UNCERTAINITY/Riloff/validation.json split: validation - config_name: uncertainity-swamy data_files: - path: data/UNCERTAINITY/Swamy/test.json split: test - path: data/UNCERTAINITY/Swamy/train.json split: train - path: data/UNCERTAINITY/Swamy/validation.json split: validation dataset_info: features: - name: tweet_id dtype: string - name: id dtype: int32 - name: text dtype: string - name: label dtype: string - name: tokens sequence: string - name: ner_tags sequence: string --- # SocialMediaIE - Social Media Information Extraction # List of datasets used for training SocialMediaIE - [Dataset referencs](#dataset-referencs) * [Tagging datasets](#tagging-datasets) - [Dataset statistics](#dataset-statistics) * [Sentiment](#sentiment) * [Abusive](#abusive) * [Uncertainity](#uncertainity) * [Part of Speech Tagging](#part-of-speech-tagging) * [Named Entity Recognition](#named-entity-recognition) * [Chunking](#chunking) * [Supersense Tagging](#supersense-tagging) - [Dataset references](#dataset-references) Table of contents generated with markdown-toc ## Dataset referencs ### Tagging datasets * **POS tagging:** [17,18] (OW), [7] (TIE), [20] (RT), [15](TB), [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 * [6] Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017.Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition.InProceedings of the 3rd Workshop on Noisy User-generated Text. Association forComputational Linguistics, Copenhagen, Denmark, 140–147. https://doi.org/10.18653/v1/W17-4418 * [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. 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