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Python implementation of bag-of-concepts

License: MIT License

Python 100.00%

bag-of-concepts text-mining document-representation document-classification machine-learning unsupervised-learning representation-learning datamining clustering word2vec concept

bagofconcepts's Introduction

BOC (Bag-of-Concepts)

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.

Installation

$ pip install bagofconcepts

Basic Usage

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()

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bagofconcepts's Issues

setup.py download_url 설정

download_url 이 되어있지 않습니다.

setup.py url 주소가 redirect 됩니다.

현재 setup.py 파일에 url = 'https://github.com/hank110/boc' 로 설정되어 있는데, hank110/bagofconcepts 로 redirect 됩니다. 이왕이면 그 주소로 적는게 더 좋을 것 같습니다.

Include different options for inputs

Needs to incorporate for the case in which the input is a numpy array

tutorial update

현재 boc package 를 이용하는 예시로 튜토리얼이 만들어져 있습니다. bagofconcepts 을 이용하는 튜토리얼로 업데이트 부탁드립니다.

PageRank 파일 정리

pagerank.py 파일이 있는데, boc 에서는 이용중이지 않아 보입니다.
다른 작업을 위해 사용할 예정이라면, 스크립트 형식이 아닌 함수 형식으로 정리가 필요해 보입니다.

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