Welcome to the InfoSci Platform
Reference Hub2
Efficient Weighted Semantic Score Based on the Huffman Coding Algorithm and Knowledge Bases for Word Sequences Embedding

Efficient Weighted Semantic Score Based on the Huffman Coding Algorithm and Knowledge Bases for Word Sequences Embedding

Nada Ben-Lhachemi, El Habib Nfaoui
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 17
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799805243|DOI: 10.4018/IJSWIS.2020040107
Cite Article Cite Article

MLA

Ben-Lhachemi, Nada, and El Habib Nfaoui. "Efficient Weighted Semantic Score Based on the Huffman Coding Algorithm and Knowledge Bases for Word Sequences Embedding." IJSWIS vol.16, no.2 2020: pp.126-142. http://doi.org/10.4018/IJSWIS.2020040107

APA

Ben-Lhachemi, N. & Nfaoui, E. H. (2020). Efficient Weighted Semantic Score Based on the Huffman Coding Algorithm and Knowledge Bases for Word Sequences Embedding. International Journal on Semantic Web and Information Systems (IJSWIS), 16(2), 126-142. http://doi.org/10.4018/IJSWIS.2020040107

Chicago

Ben-Lhachemi, Nada, and El Habib Nfaoui. "Efficient Weighted Semantic Score Based on the Huffman Coding Algorithm and Knowledge Bases for Word Sequences Embedding," International Journal on Semantic Web and Information Systems (IJSWIS) 16, no.2: 126-142. http://doi.org/10.4018/IJSWIS.2020040107

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Learning text representation is forming a core for numerous natural language processing applications. Word embedding is a type of text representation that allows words with similar meaning to have similar representation. Word embedding techniques categorize semantic similarities between linguistic items based on their distributional properties in large samples of text data. Although these techniques are very efficient, handling semantic and pragmatics ambiguity with high accuracy is still a challenging research task. In this article, we propose a new feature as a semantic score which handles ambiguities between words. We use external knowledge bases and the Huffman Coding algorithm to compute this score that depicts the semantic relatedness between all fragments composing a given text. We combine this feature with word embedding methods to improve text representation. We evaluate our method on a hashtag recommendation system in Twitter where text is noisy and short. The experimental results demonstrate that, compared with state-of-the-art algorithms, our method achieves good results.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.