Google Research Blog: From Words to Concepts and Back: Dictionaries for Linking Text, Entities and IdeasPosted by Valentin Spitkovsky and Peter Norvig, Research Team
|Yet in each word some concept there must be...|
|— from Goethe's Faust (Part I, Scene III)|
Human language is both rich and ambiguous.
When we hear or read words, we resolve meanings to mental representations,
for example recognizing and linking names to the intended persons, locations or organizations.
Bridging words and meaning —
from turning search queries into relevant results to suggesting targeted keywords for advertisers —
is also Google's core competency, and
important for many other tasks in information retrieval and natural language processing.
We are happy to release a resource,
spanning 7,560,141 concepts and 175,100,788 unique text strings,
that we hope will help everyone working in these areas.
How do we represent concepts? Our approach piggybacks on
the unique titles of entries from an encyclopedia, which are mostly proper and common noun phrases.
We consider each individual Wikipedia article
as representing a concept (an entity or an idea), identified by its URL. Text strings that refer to
concepts were collected using the publicly available hypertext of anchors (the text you click on in a web link)
that point to each Wikipedia page, thus drawing on the vast link structure of the web.
For every English article we harvested the strings associated
with its incoming hyperlinks from the rest of Wikipedia, the greater web,
and also anchors of parallel, non-English Wikipedia pages.
Our dictionaries are cross-lingual, and
any concept deemed too fine can be broadened to a desired level of generality using
groupings of articles into hierarchical categories.
The data set contains triples, each consisting of
(i) text, a short, raw natural language string;
(ii) url, a related concept, represented by an
English Wikipedia article's canonical location;
and (iii) count, an integer indicating the number of times
text has been observed connected with the concept's url.
Our database thus includes weights that measure degrees of association.
For example, the top two entries for football indicate
that it is an ambiguous term, which is almost twice as likely
to refer to what we in the US call soccer:
An inverted index can be
used to perform reverse look-ups, identifying salient terms for each concept.
Some of the highest-scoring strings — including synonyms and translations —
for both sports, are listed below:
Associated counts can easily be turned into percentages.
The following table illustrates
the concept-to-words dictionary direction —
which may be useful for paraphrasing,
and topic modeling
— for the idea of soft drink,
restricted to English (and normalized for punctuation, pluralization and capitalization differences):
|1.||soft drink||(and soft-drinks)||28.6|
|5.||carbonated beverages||(and beverage)||0.3|
|9.||carbonated soft drink||(and drinks)||0.1|
|11.||non-alcoholic drinks||(and drink)||0.1|
|12.||soft drink controversy||0.0|
|15.||soft drink topics||0.0|
The words-to-concepts dictionary direction can
and link entities, which are often highly ambiguous,
since people, places and organizations can (nearly) all be named after each other.
The next table shows the top concepts meant by the
string Stanford, which refers to all three (and other) types:
|2.||Stanford (disambiguation)||7.7||a disambiguation page|
|4.||Stanford Cardinal football||5.7||ORGANIZATION|
|5.||Stanford Cardinal||4.1||multiple athletic programs|
|6.||Stanford Cardinal men's basketball||2.0||ORGANIZATION|
|7.||Stanford prison experiment||2.0||a famous psychology experiment|
|10.||Bank of the West Classic||1.0||a recurring sporting event|
|13.||Charles Villiers Stanford||0.8||PERSON|
|14.||Stanford, New York||0.8||LOCATION|
The database that we are providing was designed for recall.
It is large and noisy, incorporating 297,073,139 distinct
string-concept pairs, aggregated over 3,152,091,432 individual
links, many of them referencing non-existent articles.
For technical details, see our paper
(to be presented at LREC 2012)
and the README file accompanying the data.
We hope that this release will fuel numerous creative applications that haven't been previously thought of!