training-data

Training Data for the NLPContributionGraph Shared Task 11 at SemEval-2021

The repository is organized as follows:

README.md                            
[task-name-folder]/                                # natural_language_inference, paraphrase_generation, question_answering, relation_extraction, topic_models
    ├── [article-counter-folder]/                  # ranges between 0 to 100 since we annotated varying numbers of articles per task
    │   ├── [articlename].pdf                      # scholarly article pdf
    │   ├── [articlename]-Grobid-out.txt           # plaintext output from the [Grobid parser](https://github.com/kermitt2/grobid)
    │   ├── [articlename]-Stanza-out.txt           # plaintext preprocessed output from [Stanza](https://github.com/stanfordnlp/stanza)
    │   ├── sentences.txt                          # annotated Contribution sentences in the file
    │   ├── entities.txt                           # annotated entities in the Contribution sentences
    │   └── info-units/                            # the folder containing information units in JSON format
    │   │   └── research-problem.json              # `research problem` mandatory information unit in json format
    │   │   └── model.json                         # `model` information unit in json format; in some articles it is called `approach`
    │   │   └── ...                                # there are 12 information units in all and each article may be annotated by 3 or 6
    │   └── triples/                               # the folder containing information unit triples one per line
    │   │   └── research-problem.txt               # `research problem` triples (one research problem statement per line)
    │   │   └── model.txt                          # `model` triples (one statement per line)
    │   │   └── ...                                # there are 12 information units in all and each article may be annotated by 3 or 6
    │   └── ...                                    # there are between 1 to 100 articles annotated for each task, so this repeats for the remaining annotated articles
    └── ...                                        # there are 24 tasks selected overall, so this repeats 23 more times

Data Element Counts

Total Papers Annotated: 237

	Tasks	info-units	sentences	entities	total triples	total unique triples	subject	predicate	object
1	natural_language_inference	427	2168	12657	7969	7330	3171	1251	5242
2	negation_scope_resolution	4	28	163	94	94	50	42	80
3	paraphrase_generation	9	44	293	177	175	99	77	160
4	part-of-speech_tagging	36	144	804	501	479	249	156	401
5	passage_re-ranking	8	32	214	126	123	63	66	103
6	phrase_grounding	5	29	172	102	102	58	53	94
7	prosody_prediction	5	31	172	105	103	58	43	97
8	query_wellformedness	5	11	54	35	35	22	25	33
9	question_answering	30	194	1059	665	640	332	203	547
10	question_generation	7	34	133	87	87	45	44	74
11	question_similarity	4	16	82	51	51	30	26	49
12	relation_extraction	69	346	1923	1154	1084	552	372	922
13	sarcasm_detection	10	40	225	138	136	77	73	116
14	semantic_parsing	12	60	275	183	180	91	74	157
15	semantic_role_labeling	22	100	545	338	318	163	137	288
16	sentence_classification	15	85	513	300	297	167	134	273
17	sentence_compression	19	77	426	260	248	138	104	223
18	sentiment_analysis	240	1275	7452	4517	4086	1864	940	2967
19	smile_recognition	3	17	85	54	54	29	34	49
20	temporal_information_extraction	8	26	152	94	93	58	62	85
21	text-to-speech_synthesis	13	69	316	197	192	103	98	174
22	text_generation	24	129	704	431	420	222	165	351
23	text_summarization	70	347	1777	1077	1010	513	346	825
24	topic_models	5	18	75	48	48	30	28	48

Note

For system training, participants are encouraged to merge the 50 files additionally from the trial-data release.

hardikarora17 / training-data Goto Github PK

training-data's Introduction

training-data

Data Element Counts

Note

training-data's People

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