--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: [] task_categories: - token-classification - text-classification task_ids: - named-entity-recognition - multi-class-classification pretty_name: ScienceIE is a dataset for the SemEval task of extracting key phrases and relations between them from scientific documents tags: - research papers - scientific papers dataset_info: - config_name: ner features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': O '1': B-Material '2': I-Material '3': B-Process '4': I-Process '5': B-Task '6': I-Task splits: - name: train num_bytes: 1185658 num_examples: 2388 - name: validation num_bytes: 204095 num_examples: 400 - name: test num_bytes: 399069 num_examples: 838 download_size: 391944 dataset_size: 1788822 - config_name: re features: - name: id dtype: string - name: tokens dtype: string - name: arg1_start dtype: int32 - name: arg1_end dtype: int32 - name: arg1_type dtype: string - name: arg2_start dtype: int32 - name: arg2_end dtype: int32 - name: arg2_type dtype: string - name: relation dtype: class_label: names: '0': O '1': Synonym-of '2': Hyponym-of splits: - name: train num_bytes: 11737101 num_examples: 24556 - name: validation num_bytes: 2347796 num_examples: 4838 - name: test num_bytes: 2835275 num_examples: 6618 download_size: 870786 dataset_size: 16920172 - config_name: science_ie features: - name: id dtype: string - name: text dtype: string - name: keyphrases list: - name: id dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: type dtype: class_label: names: '0': Material '1': Process '2': Task - name: type_ dtype: string - name: relations list: - name: arg1 dtype: string - name: arg2 dtype: string - name: relation dtype: class_label: names: '0': O '1': Synonym-of '2': Hyponym-of - name: relation_ dtype: string splits: - name: train num_bytes: 640060 num_examples: 350 - name: validation num_bytes: 112588 num_examples: 50 - name: test num_bytes: 206857 num_examples: 100 download_size: 441167 dataset_size: 959505 - config_name: subtask_a features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': O '1': B '2': I splits: - name: train num_bytes: 1185658 num_examples: 2388 - name: validation num_bytes: 204095 num_examples: 400 - name: test num_bytes: 399069 num_examples: 838 download_size: 384454 dataset_size: 1788822 - config_name: subtask_b features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: class_label: names: '0': O '1': M '2': P '3': T splits: - name: train num_bytes: 1185658 num_examples: 2388 - name: validation num_bytes: 204095 num_examples: 400 - name: test num_bytes: 399069 num_examples: 838 download_size: 385722 dataset_size: 1788822 - config_name: subtask_c features: - name: id dtype: string - name: tokens sequence: string - name: tags sequence: sequence: class_label: names: '0': O '1': S '2': H splits: - name: train num_bytes: 20102706 num_examples: 2388 - name: validation num_bytes: 3575511 num_examples: 400 - name: test num_bytes: 6431513 num_examples: 838 download_size: 399228 dataset_size: 30109730 configs: - config_name: ner data_files: - split: train path: ner/train-* - split: validation path: ner/validation-* - split: test path: ner/test-* - config_name: re data_files: - split: train path: re/train-* - split: validation path: re/validation-* - split: test path: re/test-* - config_name: science_ie data_files: - split: train path: science_ie/train-* - split: validation path: science_ie/validation-* - split: test path: science_ie/test-* default: true - config_name: subtask_a data_files: - split: train path: subtask_a/train-* - split: validation path: subtask_a/validation-* - split: test path: subtask_a/test-* - config_name: subtask_b data_files: - split: train path: subtask_b/train-* - split: validation path: subtask_b/validation-* - split: test path: subtask_b/test-* - config_name: subtask_c data_files: - split: train path: subtask_c/train-* - split: validation path: subtask_c/validation-* - split: test path: subtask_c/test-* --- # Dataset Card for ScienceIE ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://scienceie.github.io/index.html](https://scienceie.github.io/index.html) - **Repository:** [https://github.com/ScienceIE/scienceie.github.io](https://github.com/ScienceIE/scienceie.github.io) - **Paper:** [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://arxiv.org/abs/1704.02853) - **Leaderboard:** [https://competitions.codalab.org/competitions/15898](https://competitions.codalab.org/competitions/15898) ### Dataset Summary ScienceIE is a dataset for the SemEval task of extracting key phrases and relations between them from scientific documents. A corpus for the task was built from ScienceDirect open access publications and was available freely for participants, without the need to sign a copyright agreement. Each data instance consists of one paragraph of text, drawn from a scientific paper. Publications were provided in plain text, in addition to xml format, which included the full text of the publication as well as additional metadata. 500 paragraphs from journal articles evenly distributed among the domains Computer Science, Material Sciences and Physics were selected. The training data part of the corpus consists of 350 documents, 50 for development and 100 for testing. This is similar to the pilot task described in Section 5, for which 144 articles were used for training, 40 for development and for 100 testing. There are three subtasks: - Subtask (A): Identification of keyphrases - Given a scientific publication, the goal of this task is to identify all the keyphrases in the document. - Subtask (B): Classification of identified keyphrases - In this task, each keyphrase needs to be labelled by one of three types: (i) PROCESS, (ii) TASK, and (iii) MATERIAL. - PROCESS: Keyphrases relating to some scientific model, algorithm or process should be labelled by PROCESS. - TASK: Keyphrases those denote the application, end goal, problem, task should be labelled by TASK. - MATERIAL: MATERIAL keyphrases identify the resources used in the paper. - Subtask (C): Extraction of relationships between two identified keyphrases - Every pair of keyphrases need to be labelled by one of three types: (i) HYPONYM-OF, (ii) SYNONYM-OF, and (iii) NONE. - HYPONYM-OF: The relationship between two keyphrases A and B is HYPONYM-OF if semantic field of A is included within that of B. One example is Red HYPONYM-OF Color. - SYNONYM-OF: The relationship between two keyphrases A and B is SYNONYM-OF if they both denote the same semantic field, for example Machine Learning SYNONYM-OF ML. Note: The default config `science_ie` converts the original .txt & .ann files to a dictionary format that is easier to use. For every other configuration the documents were split into sentences using spaCy, resulting in a 2388, 400, 838 split. The `id` consists of the document id and the example index within the document separated by an underscore, e.g. `S0375960115004120_1`. This should enable you to reconstruct the documents from the sentences. ### Supported Tasks and Leaderboards - **Tasks:** Key phrase extraction and relation extraction in scientific documents - **Leaderboards:** [https://competitions.codalab.org/competitions/15898](https://competitions.codalab.org/competitions/15898) ### Languages The language in the dataset is English. ## Dataset Structure ### Data Instances #### science_ie An example of "train" looks as follows: ```json { "id": "S221266781300018X", "text": "Amodel are proposed for modeling data-centric Web services which are powered by relational databases and interact with users according to logical formulas specifying input constraints, control-flow constraints and state/output/action rules. The Linear Temporal First-Order Logic (LTL-FO) formulas over inputs, states, outputs and actions are used to express the properties to be verified.We have proven that automatic verification of LTL-FO properties of data-centric Web services under input-bounded constraints is decidable by reducing Web services to data-centric Web applications. Thus, we can verify Web service specifications using existing verifier designed for Web applications.", "keyphrases": [ { "id": "T1", "start": 24, "end": 58, "type": 2, "type_": "Task" }, ..., {"id": "T3", "start": 245, "end": 278, "type": 1, "type_": "Process"}, {"id": "T4", "start": 280, "end": 286, "type": 1, "type_": "Process"}, ... ], "relations": [ {"arg1": "T4", "arg2": "T3", "relation": 1, "relation_": "Synonym-of"}, {"arg1": "T3", "arg2": "T4", "relation": 1, "relation_": "Synonym-of"} ] } ``` #### subtask_a An example of "train" looks as follows: ```json { "id": "S0375960115004120_1", "tokens": ["Another", "remarkable", "feature", "of", "the", "quantum", "field", "treatment", "can", "be", "revealed", "from", "the", "investigation", "of", "the", "vacuum", "state", "."], "tags": [0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0] } ``` #### subtask_b An example of "train" looks as follows: ```json { "id": "S0375960115004120_2", "tokens": ["For", "a", "classical", "field", ",", "vacuum", "is", "realized", "by", "simply", "setting", "the", "potential", "to", "zero", "resulting", "in", "an", "unaltered", ",", "free", "evolution", "of", "the", "particle", "'s", "plane", "wave", "(", "|ψI〉=|ψIII〉=|k0", "〉", ")", "."], "tags": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0] } ``` #### subtask_c An example of "train" looks as follows: ```json { "id": "S0375960115004120_3", "tokens": ["In", "the", "quantized", "treatment", ",", "vacuum", "is", "represented", "by", "an", "initial", "Fock", "state", "|n0=0", "〉", "which", "still", "interacts", "with", "the", "particle", "and", "yields", "as", "final", "state", "|ΨIII", "〉", "behind", "the", "field", "region(19)|ΨI〉=|k0〉⊗|0〉⇒|ΨIII〉=∑n=0∞t0n|k−n〉⊗|n", "〉", "with", "a", "photon", "exchange", "probability(20)P0,n=|t0n|2=1n!e−Λ2Λ2n", "The", "particle", "thus", "transfers", "energy", "to", "the", "vacuum", "field", "leading", "to", "a", "Poissonian", "distributed", "final", "photon", "number", "."], "tags": [[0, 0, ...], [0, 0, ...], ...] } ``` Note: The tag sequence consists of vectors for each token, that encode what the relationship between that token and every other token in the sequence is for the first token in each key phrase. #### ner An example of "train" looks as follows: ```json { "id": "S0375960115004120_4", "tokens": ["Let", "'s", "consider", ",", "for", "example", ",", "a", "superconducting", "resonant", "circuit", "as", "source", "of", "the", "field", "."], "tags": [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0] } ``` #### re An example of "train" looks as follows: ```json { "id": "S0375960115004120_5", "tokens": ["In", "the", "quantized", "treatment", ",", "vacuum", "is", "represented", "by", "an", "initial", "Fock", "state", "|n0=0", "〉", "which", "still", "interacts", "with", "the", "particle", "and", "yields", "as", "final", "state", "|ΨIII", "〉", "behind", "the", "field", "region(19)|ΨI〉=|k0〉⊗|0〉⇒|ΨIII〉=∑n=0∞t0n|k−n〉⊗|n", "〉", "with", "a", "photon", "exchange", "probability(20)P0,n=|t0n|2=1n!e−Λ2Λ2n", "The", "particle", "thus", "transfers", "energy", "to", "the", "vacuum", "field", "leading", "to", "a", "Poissonian", "distributed", "final", "photon", "number", "."], "arg1_start": 2, "arg1_end": 4, "arg1_type": "Task", "arg2_start": 5, "arg2_end": 6, "arg2_type": "Material", "relation": 0 } ``` ### Data Fields #### science_ie - `id`: the instance id of this document, a `string` feature. - `text`: the text of this document, a `string` feature. - `keyphrases`: the list of keyphrases of this document, a `list` of `dict`. - `id`: the instance id of this keyphrase, a `string` feature. - `start`: the character offset start of this keyphrase, an `int` feature. - `end`: the character offset end of this keyphrase, exclusive, an `int` feature. - `type`: the key phrase type of this keyphrase, a classification label. - `type_`: the key phrase type of this keyphrase, a `string` feature. - `relations`: the list of relations of this document, a `list` of `dict`. - `arg1`: the instance id of the first keyphrase, a `string` feature. - `arg2`: the instance id of the second keyphrase, a `string` feature. - `relation`: the relation label of this instance, a classification label. - `relation_`: the relation label of this instance, a `string` feature. Keyphrase types: ```json {"O": 0, "Material": 1, "Process": 2, "Task": 3} ``` Relation types: ```json {"O": 0, "Synonym-of": 1, "Hyponym-of": 2} ``` #### subtask_a - `id`: the instance id of this sentence, a `string` feature. - `tokens`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features. - `tags`: the list of tags of this sentence marking a token as being outside, at the beginning, or inside a key phrase, a `list` of classification labels. ```json {"O": 0, "B": 1, "I": 2} ``` #### subtask_b - `id`: the instance id of this sentence, a `string` feature. - `tokens`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features. - `tags`: the list of tags of this sentence marking a token as being outside a key phrase, or being part of a material, process or task, a `list` of classification labels. ```json {"O": 0, "M": 1, "P": 2, "T": 3} ``` #### subtask_c - `id`: the instance id of this sentence, a `string` feature. - `tokens`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features. - `tags`: a vector for each token, that encodes what the relationship between that token and every other token in the sequence is for the first token in each key phrase, a `list` of a `list` of a classification label. ```json {"O": 0, "S": 1, "H": 2} ``` #### ner - `id`: the instance id of this sentence, a `string` feature. - `tokens`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features. - `tags`: the list of ner tags of this sentence, a `list` of classification labels. ```json {"O": 0, "B-Material": 1, "I-Material": 2, "B-Process": 3, "I-Process": 4, "B-Task": 5, "I-Task": 6} ``` #### re - `id`: the instance id of this sentence, a `string` feature. - `token`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features. - `arg1_start`: the 0-based index of the start token of the relation arg1 mention, an `ìnt` feature. - `arg1_end`: the 0-based index of the end token of the relation arg1 mention, exclusive, an `ìnt` feature. - `arg1_type`: the key phrase type of the end token of the relation arg1 mention, a `string` feature. - `arg2_start`: the 0-based index of the start token of the relation arg2 mention, an `ìnt` feature. - `arg2_end`: the 0-based index of the end token of the relation arg2 mention, exclusive, an `ìnt` feature. - `arg2_type`: the key phrase type of the relation arg2 mention, a `string` feature. - `relation`: the relation label of this instance, a classification label. ```json {"O": 0, "Synonym-of": 1, "Hyponym-of": 2} ``` ### Data Splits | | Train | Dev | Test | |------------|-------|------|------| | science_ie | 350 | 50 | 100 | | subtask_a | 2388 | 400 | 838 | | subtask_b | 2388 | 400 | 838 | | subtask_c | 2388 | 400 | 838 | | ner | 2388 | 400 | 838 | | re | 24558 | 4838 | 6618 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{DBLP:journals/corr/AugensteinDRVM17, author = {Isabelle Augenstein and Mrinal Das and Sebastian Riedel and Lakshmi Vikraman and Andrew McCallum}, title = {SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications}, journal = {CoRR}, volume = {abs/1704.02853}, year = {2017}, url = {http://arxiv.org/abs/1704.02853}, eprinttype = {arXiv}, eprint = {1704.02853}, timestamp = {Mon, 13 Aug 2018 16:46:36 +0200}, biburl = {https://dblp.org/rec/journals/corr/AugensteinDRVM17.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.