WordNet2Vec: Corpora agnostic word vectorization method

Roman Bartusiak

doi:10.1016/j.neucom.2017.01.121

Return

WordNet2Vec: Corpora agnostic word vectorization method

2019-01-01

DOI：10.1016/j.neucom.2017.01.121

Original

Save

View PDF

Abstract

En 中文

The complex nature of big data resources requires new structuring methods, especially for textual content. WordNet is a good knowledge source for the comprehensive abstraction of natural language as it offers good implementation for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism, WordNet2Vec, is proposed in this paper. This creates vectors for each word from WordNet. These vectors encapsulate a general position - the role of a given word related to all other words in the given natural language. Any list or set of such vectors contains knowledge about the context of its components within the whole language. This type of word representation can be easily applied to many analytic tasks such as classification or clustering. The usefulness of the WordNet2Vec method is demonstrated in sentiment analysis including the classification of an Amazon opinion text dataset with transfer learning. (C) 2017 Elsevier B.V. All rights reserved.

Keywords:

Natural language structuring

WordNet

WordNet2Vec

Vectorization

Network transformation

Sentiment analysis

Transfer learning

Big data

Complex networks

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.