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50,000 Lessons on How to Read: a Relation Extraction Corpus
Thursday, April 11, 2013
Posted by Dave Orr, Product Manager, Google Research
One of the most difficult tasks in NLP is called
relation extraction.
It’s an example of information extraction, one of the goals of natural language understanding. A relation is a semantic connection between (at least) two entities. For instance, you could say that
Jim Henson
was in a spouse relation with
Jane Henson
(and in a creator relation with
many
beloved
characters
and
shows
).
The goal of relation extraction is to learn relations from unstructured natural language text. The relations can be used to answer questions (“
Who created Kermit?
”), learn
which proteins interact
in the biomedical literature, or to build a database of
hundreds of millions of entities and billions of relations
to try and help people
explore the world’s information
.
To help researchers investigate relation extraction, we’re releasing a
human-judged dataset
of two relations about public figures on
Wikipedia
: nearly 10,000 examples of “place of birth”, and over 40,000 examples of “attended or graduated from an institution”. Each of these was judged by at least 5 raters, and can be used to train or evaluate relation extraction systems. We also plan to release more relations of new types in the coming months.
(Update: you can find additional relations
here
.)
Each relation is in the form of a triple: the relation in question, called a predicate; the subject of the relation; and the object of the relation. In the relation “Stephen Hawking graduated from Oxford,” Stephen Hawking is the subject, graduated from is the relation, and Oxford University is the object. Subjects and objects are represented by their
Freebase MID’s
, and the relation is defined as a
Freebase property
. So in this case, the triple would be represented as:
"pred":"
/education/education/institution
"
"sub":"
/m/01tdnyh
"
"obj":"
/m/07tgn
"
Just having the triples is interesting enough if you want a database of entities and relations, but doesn’t make much progress towards training or evaluation a relation extraction system. So we’ve also included the evidence for the relation, in the form of a URL and an excerpt from the web page that our raters judged. We’re also including examples where the evidence does not support the relation, so you have negative examples for use in training better extraction systems. Finally, we included ID’s and actual judgments of individual raters, so that you can filter triples by agreement.
Gory Details
The corpus itself, extracted from Wikipedia, can be found here:
https://code.google.com/p/relation-extraction-corpus/
The files are in
JSON
format. Each line is a triple with the following fields:
pred: predicate of a triple
sub: subject of a triple
obj: object of a triple
evidences: an array of evidences for this triple
url: the web page from which this evidence was obtained
snippet: short piece of text supporting the triple
judgments: an array of judgements from human annotators
rator: hash code of the identity of the annotator
judgment: judgement of the annotator. It can take the values "yes" or "no"
Here’s an example:
{"pred":"/people/person/place_of_birth","sub":"/m/026_tl9","obj":"/m/02_286","evidences":[{"url":"http://en.wikipedia.org/wiki/Morris_S._Miller","snippet":"Morris Smith Miller (July 31, 1779 -- November 16, 1824) was a United States Representative from New York. Born in New York City, he graduated from Union College in Schenectady in 1798. He studied law and was admitted to the bar. Miller served as private secretary to Governor Jay, and subsequently, in 1806, commenced the practice of his profession in Utica. He was president of the village of Utica in 1808 and judge of the court of common pleas of Oneida County from 1810 until his death."}],"judgments":[{"rater":"11595942516201422884","judgment":"yes"},{"rater":"16169597761094238409","judgment":"yes"},{"rater":"1014448455121957356","judgment":"yes"},{"rater":"16651790297630307764","judgment":"yes"},{"rater":"1855142007844680025","judgment":"yes"}]}
The web is chock full of information, put there to be read and learned from. Our hope is that this corpus is a small step towards computational understanding of the wealth of relations to be found everywhere you look.
This dataset is licensed by Google Inc. under the
Creative Commons Attribution-Sharealike 3.0
license.
Thanks to Shaohua Sun, Ni Lao, and Rahul Gupta for putting this dataset together.
Thanks also to Michael Ringgaard, Fernando Pereira, Amar Subramanya, Evgeniy Gabrilovich, and John Giannandrea for making this data release possible.
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