Task Categories: other
Languages: en
Multilinguality: monolingual
Size Categories: n>1M
Language Creators: crowdsourced
Annotations Creators: machine-generated
Source Datasets: original

Dataset Card for Ollie

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:



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.


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:

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} }


Thanks to @ontocord for adding this dataset.

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