Open AccessSemantics Graph Mining for Topic Discovery and Word Associations
Author(s) -
Alex Romanova
Publication year - 2021
Publication title -
international journal of data mining and knowledge management process
Language(s) - English
Resource type - Journals
eISSN - 2231-007X
pISSN - 2230-9608
DOI - 10.5121/ijdkp.2021.11401
Subject(s) - computer science , word2vec , word embedding , natural language processing , artificial intelligence , word (group theory) , semantics (computer science) , graph , information retrieval , embedding , theoretical computer science , linguistics , philosophy , programming language
Big Data creates many challenges for data mining experts, in particular in getting meanings of text data. It is beneficial for text mining to build a bridge between word embedding process and graph capacity to connect the dots and represent complex correlations between entities. In this study we examine processes of building a semantic graph model to determine word associations and discover document topics. We introduce a novel Word2Vec2Graph model that is built on top of Word2Vec word embedding model. We demonstrate how this model can be used to analyze long documents, get unexpected word associations and uncover document topics. To validate topic discovery method we transfer words to vectors and vectors to images and use CNN deep learning image classification.