| | --- |
| | 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. |
