该所有者的项目
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% Code %
% Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection %
% Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning %
% Advances in Neural Information Processing Systems (NIPS 2011) %
% See http://www.socher.org for more information or to ask questions %
% %
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This code computes phrase vectors based on a trained,
unfolding recursive neural network as described in
the above paper.
It is designed to be easy to use, all you need to do
is to put phrases for which you want to compute a
compositional vector into a text file, one phrase or
sentence per line. The output will be another
textfile with the vectors.
This code is provided as is. It is free for
academic, non-commercial purposes.
For questions, please contact richard @ socher .org
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% Installation %
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The code runs on any linux machine with bash,
matlab and java installed. -
After unpacking the zip file go to folder and make
sure the executables have permission:
chmod 777 phrase2Vector.sh
chmod 777 stanford-parser-2011-09-14/lexparser.sh
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% Running the Code %
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You can see how it creates vectors for phrases
in the input.txt file by just running:./phrase2Vector.sh
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To get phrase vectors for your own phrases,
you need to change the fileinput.txt
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Each line of the input.txt file should contain a
phrase or sentence. -
The code will the produce as output a text file:
outVectors.txt
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In this file, the nth line of the file is the
vector for the nth phrase in the input.txt file../phrase2Vector.sh
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For debugging purposes, the program also output
the file phrases.txt, which shows which words
were unknown the the vocabulary. -
In summary:
INPUT
input.txt: one phrase or sentence per line for
which you want to compute vector representations
OUTPUT
outVectors.txt - the nth line of the file is
the vector for the nth phrase
phrases.txt - shows which words were in our
dictionary and which ones are unknown
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% Included Packages %
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This archive includes 2 external packages for convenience:
-
The word vectors of the paper "Word representations:
A simple and general method for semi-supervised
learning" by Turian et al (ACL, 2010).
These word vectors are available at:
http://metaoptimize.com/projects/wordreprs/ -
The Stanford Parser of the paper "Accurate
Unlexicalized Parsing" by Dan Klein and
Christopher D. Manning. 2003.
The parser is available at
http://nlp.stanford.edu/software/lex-parser.shtml
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% Bibtex %
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If you use the code, please cite:
@incollection{SocherEtAl2011:PoolRAE,
title = {{Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection}},
author = {Richard Socher and Eric H. Huang and Jeffrey Pennington and Andrew Y. Ng and Christopher D. Manning},
booktitle = {{Advances in Neural Information Processing Systems 24}},
year = {2011}
}