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EMBeddings with Domain Awareness (EMBDA)

This repository contains source code necessary to reproduce the results presented in the paper Learning Word Embeddings with Domain Awareness.

The code is extended from origin word2vec repository. For more information about the origin word2vec algorith and code base requirement, please refer to word2vec.

To build the code, simply run:


The command to build word embeddings is exactly the same as in the original version, except that we removed the argument -cbow and replaced it with the argument -type:

./embda -train input_file -word-pair-file word_pair_file -output embedding_file -type 0 -size 50 -window 5 -negative 10 -hs 0 -sample 1e-4 -threads 1 -binary 1 -iter 5

The -word-pair-file option specify the file with word pairs extracted from the target domain. Note that each line the word pair file uses the following format:

w num [w1 w2 ... wn]

where w is the word, num denotes how many word pairs w has, if num is not zero, then w1 w2 ... wn are the words paired with w. Some example lines are:

bijz 0
nsicop 0
time 10 squares callin floating winter think ...

The lines in the word pair file should be the same with the vocabulary file for embeddings, i.e., the one use -save-vocab option generated.

The -type argument is an integer that defines which algorithm to use. These are the possible parameters:

0 - the skip-gram model with domain indicator;
1 - the continuous bag-of-words with domain attention


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