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A Multilingual Corpus of Automatically Extracted Relations from Wikipedia
Tuesday, June 2, 2015
Posted by Shankar Kumar, Google Research Scientist and Manaal Faruqui, Carnegie Mellon University PhD candidate
In
Natural Language Processing
, relation extraction is the task of assigning a semantic relationship between a pair of arguments. As an example, a relationship between the phrases “
Ottawa
” and “
Canada
” is “
is the capital of
”. These extracted relations could be used in a variety of applications ranging from
Question Answering
to building databases from unstructured text.
While relation extraction systems work accurately for English and a few other languages, where tools for syntactic analysis such as parsers, part-of-speech taggers and named entity analyzers are readily available, there is relatively little work in developing such systems for most of the world's languages where linguistic analysis tools do not yet exist. Fortunately, because we do have translation systems between English and many other languages (such as
Google Translate
), we can translate text from a non-English language to English, perform relation extraction and project these relations back to the foreign language.
Relation extraction in a Spanish sentence using the cross-lingual relation extraction pipeline.
In
Multilingual Open Relation Extraction Using Cross-lingual Projection
, that will appear at the
2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies
(NAACL HLT 2015), we use this idea of cross-lingual projection to develop an algorithm that extracts open-domain relation
tuples
, i.e. where an arbitrary phrase can describe the relation between the arguments, in multiple languages from
Wikipedia
. In this work, we also evaluated the performance of extracted relations using human annotations in French, Hindi and Russian.
Since there is no such publicly available corpus of multilingual relations, we are
releasing a dataset
of automatically extracted relations from the Wikipedia corpus in 61 languages, along with the manually annotated relations in 3 languages (French, Hindi and Russian). It is our hope that our data will help researchers working on natural language processing and encourage novel applications in a wide variety of languages.
We wish to thank Bruno Cartoni, Vitaly Nikolaev, Hidetoshi Shimokawa, Kishore Papineni, John Giannandrea and their teams for making this data release possible. This dataset is licensed by Google Inc. under the
Creative Commons Attribution-ShareAlike 3.0 License
.
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