This is python implementation of Bag-of-Concepts, as proposed in the paper "Bag-of-Concepts: Comprehending Document Representation through Clustering Words in Distributed Representation" (Han Kyul Kim, Hyunjoong Kim, Sunzoon Cho)
For a given text data, it trains word2vec vectors for each of the words and clusters semantically similar words into a common "concept".
Subsequently, each document is represented by the counts of these concepts with concept frequency - inverse document frequency weighting scheme.
$ pip install bagofconcepts
import bagofconcepts as boc # Each line of corpus must be equivalent to each document of the corpus boc_model=boc.BOCModel(doc_path="input corpus path") # output can be saved with save_path parameter boc_matrix,word2concept_list,idx2word_converter=boc_model.fit()