Datasets:
Languages:
English
Multilinguality:
monolingual
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
original
License:
| annotations_creators: | |
| - machine-generated | |
| language_creators: | |
| - crowdsourced | |
| language: | |
| - en | |
| license: | |
| - other | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10M<n<100M | |
| - 1M<n<10M | |
| source_datasets: | |
| - original | |
| task_categories: [] | |
| task_ids: [] | |
| pretty_name: Ollie | |
| tags: | |
| - relation-extraction | |
| - text-to-structured | |
| dataset_info: | |
| - config_name: ollie_lemmagrep | |
| features: | |
| - name: arg1 | |
| dtype: string | |
| - name: arg2 | |
| dtype: string | |
| - name: rel | |
| dtype: string | |
| - name: search_query | |
| dtype: string | |
| - name: sentence | |
| dtype: string | |
| - name: words | |
| dtype: string | |
| - name: pos | |
| dtype: string | |
| - name: chunk | |
| dtype: string | |
| - name: sentence_cnt | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 12324648919 | |
| num_examples: 18674630 | |
| download_size: 1789363108 | |
| dataset_size: 12324648919 | |
| - config_name: ollie_patterned | |
| features: | |
| - name: rel | |
| dtype: string | |
| - name: arg1 | |
| dtype: string | |
| - name: arg2 | |
| dtype: string | |
| - name: slot0 | |
| dtype: string | |
| - name: search_query | |
| dtype: string | |
| - name: pattern | |
| dtype: string | |
| - name: sentence | |
| dtype: string | |
| - name: parse | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 2930309084 | |
| num_examples: 3048961 | |
| download_size: 387514061 | |
| dataset_size: 2930309084 | |
| config_names: | |
| - ollie_lemmagrep | |
| - ollie_patterned | |
| # Dataset Card for Ollie | |
| ## 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:** [Ollie](https://knowitall.github.io/ollie/) | |
| - **Repository:** [Github](https://github.com/knowitall/ollie) | |
| - **Paper:** [Aclweb](https://www.aclweb.org/anthology/D12-1048/) | |
| ### Dataset Summary | |
| The Ollie dataset includes two configs for the data | |
| used to train the Ollie informatation extraction algorithm, for 18M | |
| sentences and 3M sentences respectively. | |
| This data is for academic use only. From the authors: | |
| Ollie is a program that automatically identifies and extracts binary | |
| relationships from English sentences. Ollie is designed for Web-scale | |
| information extraction, where target relations are not specified in | |
| advance. | |
| Ollie is our second-generation information extraction system . Whereas | |
| ReVerb operates on flat sequences of tokens, Ollie works with the | |
| tree-like (graph with only small cycles) representation using | |
| Stanford's compression of the dependencies. This allows Ollie to | |
| capture expression that ReVerb misses, such as long-range relations. | |
| Ollie also captures context that modifies a binary relation. Presently | |
| Ollie handles attribution (He said/she believes) and enabling | |
| conditions (if X then). | |
| More information is available at the Ollie homepage: | |
| https://knowitall.github.io/ollie/ | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed] | |
| ### Languages | |
| en | |
| ## Dataset Structure | |
| ### Data Instances | |
| There are two configurations for the dataset: ollie_lemmagrep which | |
| are 18M sentences from web searches for a subset of the Reverb | |
| relationships (110,000 relationships), and the 3M sentences for | |
| ollie_patterned which is a subset of the ollie_lemmagrep dataset | |
| derived from patterns according to the Ollie paper. | |
| An example of an ollie_lemmagrep record: | |
| `` | |
| {'arg1': 'adobe reader', | |
| 'arg2': 'pdf', | |
| 'chunk': 'B-NP I-NP I-NP I-NP B-PP B-NP I-NP B-VP B-PP B-NP I-NP O B-VP B-NP I-NP I-NP I-NP B-VP I-VP I-VP O', | |
| 'pos': 'JJ NNS CC NNS IN PRP$ NN VBP IN NNP NN CC VB DT NNP NNP NNP TO VB VBN .', | |
| 'rel': 'be require to view', | |
| 'search_query': 'require reader pdf adobe view', | |
| 'sentence': 'Many documents and reports on our site are in PDF format and require the Adobe Acrobat Reader to be viewed .', | |
| 'sentence_cnt': '9', | |
| 'words': 'many,document,and,report,on,our,site,be,in,pdf,format,and,require,the,adobe,acrobat,reader,to,be,view'} | |
| `` | |
| An example of an ollie_patterned record: | |
| `` | |
| {'arg1': 'english', | |
| 'arg2': 'internet', | |
| 'parse': '(in_IN_6), advmod(important_JJ_4, most_RBS_3); nsubj(language_NN_5, English_NNP_0); cop(language_NN_5, being_VBG_1); det(language_NN_5, the_DT_2); amod(language_NN_5, important_JJ_4); prep_in(language_NN_5, era_NN_9); punct(language_NN_5, ,_,_10); conj(language_NN_5, education_NN_12); det(era_NN_9, the_DT_7); nn(era_NN_9, Internet_NNP_8); amod(education_NN_12, English_JJ_11); nsubjpass(enriched_VBN_15, language_NN_5); aux(enriched_VBN_15, should_MD_13); auxpass(enriched_VBN_15, be_VB_14); punct(enriched_VBN_15, ._._16)', | |
| 'pattern': '{arg1} <nsubj< {rel:NN} >prep_in> {slot0:NN} >nn> {arg2}', | |
| 'rel': 'be language of', | |
| 'search_query': 'english language internet', | |
| 'sentence': 'English being the most important language in the Internet era , English education should be enriched .', | |
| 'slot0': 'era'} | |
| `` | |
| ### Data Fields | |
| For ollie_lemmagrep: | |
| * rel: the relationship phrase/verb phrase. This may be empty, which represents the "be" relationship. | |
| * arg1: the first argument in the relationship | |
| * arg2: the second argument in the relationship. | |
| * chunk: a tag of each token in the sentence, showing the pos chunks | |
| * pos: part of speech tagging of the sentence | |
| * sentence: the sentence | |
| * sentence_cnt: the number of copies of this sentence encountered | |
| * search_query: a combintion of rel, arg1, arg2 | |
| * words: the lemma of the words of the sentence separated by commas | |
| For ollie_patterned: | |
| * rel: the relationship phrase/verb phrase. | |
| * arg1: the first argument in the relationship | |
| * arg2: the second argument in the relationship. | |
| * slot0: the third argument in the relationship, which might be empty. | |
| * pattern: a parse pattern for the relationship | |
| * parse: a dependency parse forthe sentence | |
| * search_query: a combintion of rel, arg1, arg2 | |
| * sentence: the senence | |
| ### Data Splits | |
| There are no splits. | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| This dataset was created as part of research on open information extraction. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| See the research paper on OLlie. The training data is extracted from web pages (Cluebweb09). | |
| #### Who are the source language producers? | |
| The Ollie authors at the Univeristy of Washington and data from Cluebweb09 and the open web. | |
| ### Annotations | |
| #### Annotation process | |
| The various parsers and code from the Ollie alogrithm. | |
| #### Who are the annotators? | |
| Machine annotated. | |
| ### Personal and Sensitive Information | |
| Unkown, but likely there are names of famous individuals. | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| The goal for the work is to help machines learn to extract information form open domains. | |
| ### Discussion of Biases | |
| Since the data is gathered from the web, there is likely to be biased text and relationships. | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| The authors of Ollie at The University of Washington | |
| ### Licensing Information | |
| The University of Washington academic license: https://raw.githubusercontent.com/knowitall/ollie/master/LICENSE | |
| ### Citation Information | |
| ``` | |
| @inproceedings{ollie-emnlp12, | |
| author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni}, | |
| title = {Open Language Learning for Information Extraction}, | |
| booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)}, | |
| year = {2012} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset. |