Extracting Word Synonyms from Text
using Neural Approaches
Nora Mohammed
College of Engineering, Al-Qadisiyah University, Iraq
Abstract: Extracting synonyms from textual corpora using computational techniques
is an interesting research problem in the Natural Language Processing (NLP)
domain. Neural techniques (such as Word2Vec) have been recently utilized to produce
distributional word representations (also known as word embeddings) that
capture semantic similarity/relatedness between words based on linear context.
Nevertheless, using these techniques for synonyms extraction poses many
challenges due to the fact that similarity between vector word representations does
not indicate only synonymy between words, but also other sense relations as
well as word association or relatedness. In this paper, we tackle this problem
using a novel 2-step approach. We first build distributional word embeddings
using Word2Vec then use the induced word embeddings as an input to train a feed-forward
neutral network using annotated dataset to distinguish between synonyms and
other semantically related words.
Keywords: Neural networks, semantic similarity, word
representations, natural language processing.
Received April 17, 2017;
accepted October 24, 2017
https://doi.org/10.34028/iajit/17/1/6