An ESA implementation in python.
Project description
# Get the required resources <code> scp -r webis@webislab40.medien.uni-weimar.de:/home/weci2587/projects/args-topic-modeling/resources . </code>
# To run the ESA-script with all terms run: ## For normal ESA:
- ./esa-all-terms.py –similarity cos
–matrix-path <path_to_resources>/resources/esa-plain/<debatepedia|strategic-intelligence|wikipedia>.mat –model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin –model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p –input-path <path_to_input_file> –output-path <path_to_output_file>
## For word2vec-ESA:
- ./esa-all-terms.py –similarity max
–matrix-path <path_to_resources>/resources/esa-w2v/<debatepedia|strategic-intelligence|wikipedia>.mat –model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin –model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p –input-path <path_to_input_file> –output-path <path_to_output_file>
# To run the word2vec-ESA with reduced terms run:
- ./esa-top-n-terms.py -n <number_of_terms>
–corpus-path <path_to_resources>/resources/corpora/<debatepedia|strategic-intelligence|wikipedia>.csv –model-path <path_to_resources>/resources/esa-w2v/GoogleNews-vectors-negative300.bin –model-vocab <path_to_resources>/resources/esa-w2v/w2v-vocab.p –input-path <path_to_input_file> –output-path <path_to_output_file>
The input document must be a csv file with “|” as the separator and must contain the column “document”, which is used as the input text for the ESA.
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