Minwise Hashing for Relationship Extraction from Text
The MinHash-based Semantic Relationship Classifier (MuSICo) is an on-line approach for extracting of semantic relationships, based on the idea of nearest neighbor classification.
Instead of learning a statistical model, it finds the most similar relationship instances in a database and uses these similarities to make the decision of whether the sentence holds a certain relationship type. The sentence is classified according to the relationship type of the most similar relationship instances in a database.
The computation is done by leveraging min-hash and locality sensitive hashing for efficiently measuring the similarity between instances.
ant to compile the source code, which should generate
MuSICo.jar based on
All the external libs needed by MuSICo are in the
libs/ directory. Then you can call MuSICo.jar with the following parameters, e.g.:
java -cp libs/*:MuSICo.jar bin.Main semeval true 400 50 5
MuSICo.jar bin.Main dataset true|false #min-hash-sigs #bands #kNN [train_file] [test_file] dataset semeval wiki aimed wikipt true|false generate shingles ? if false need to pass train_file/test_file #min-hash-sigs number of hash signatures #bands size of the LSH bands #kNN number of closest neighbors to consider
David S. Batista, Rui Silva, Bruno Martins, Mário J. Silva, A Minwise Hashing Method for Addressing Relationship Extraction from Text in Web Information Systems Engineering (WISE), 2013
David Soares Batista, David Forte, Rui Silva, Bruno Martins, Mário Silva, Exploring DBpedia and Wikipedia for Portuguese Semantic Relationship Extraction in Linguamática, 5(1), 2013.
David S. Batista, Ph.D. Thesis, Large-Scale Semantic Relationship Extraction for Information Discovery (Chapter 4), Instituto Superior Técnico, University of Lisbon, 2016