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Joint Bootstrapping Machines for High Confidence Relation Extraction : NAACL-HLT 2018 Long Paper.
This project forked from pgcool/joint-bootstrapping-machines
Joint Bootstrapping Machines for High Confidence Relation Extraction : NAACL-HLT 2018 Long Paper.
"Joint Bootstrapping Machines for High Confidence Relation Extraction" in conference proceedings of NAACL-HLT 2018. Dependecies: - Python 2.7, Numpy, NLTK, Gensim, jellyfish, whoosh, etc. Directory Structure: Joint-Bootstrapping-Machines resources freebase-easy-14-04-14 freebase_facts.txt - Download from url http://freebase-easy.cs.uni-freiburg.de/dump/ data input sentences.txt - Download corpus from url: https://drive.google.com/file/d/0B0CbnDgKi0PyM1FEQXJRTlZtSTg/view) output BREE REL_ACQUIRED_ORG_ORG relationships_baseline.txt - The output file containing a list of the relationships extracted from BREE system) BRET REL_ACQUIRED_ORG_ORG relationships_config5.txt - The output file containing a list of the relationships extracted from BRET system) BREJ REL_ACQUIRED_ORG_ORG relationships_config9.txt - The output file containing a list of the relationships extracted from BREJ system) code automatic_evaluation index_dir - Directory of corpus-index index_whoosh.py - To create corpus-index in directory index_dir. Sentence.py - To extract entities infomation, clean and filter. easy_freebase_clean.py - To collect relationships facts from Freebase and prepare databases. large_scale_evaluation_freebase.py - To automatically evaluate relation extraction systems on large-scale (https://akbcwekex2012.files.wordpress.com/2012/05/8_paper.pdf). Usage (Evaluation): $ python large_scale_evaluation_freebase.py threshold system_output rel_type database root_dir corpus-index Example: $ cd Joint-Bootstrapping-Machines/code/automatic_evaluation $ python large_scale_evaluation_freebase.py 0.5 ../../data/output/BREE/REL_ACQUIRED_ORG_ORG/relationships_baseline.txt acquired ../../resources/freebase-easy-14-04-14/freebase_facts.txt ../../data ../../data/input/sentences.txt ./index_dir
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