metadata
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
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
- [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. Springer,Cham, 171–186. https://doi.org/10.1007/978-3-319-18818-8{_}11
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