English Sentence Semantic Feature Extraction Method Based on Fuzzy Logic Algorithm

Abstract

Semantic features play a pivotal role in natural language processing, providing a deeper understanding of the meaning and context within textual data. In the realm of machine learning and artificial intelligence, semantic feature extraction involves translating linguistic elements into numerical representations, often utilizing advanced techniques like word embeddings and deep learning models. The integration of semantic features enhances the precision and context-awareness of language models, enabling applications such as sentiment analysis, document categorization, and information retrieval to operate with greater accuracy and relevance. The paper introduces a novel approach, Hierarchical Mandhami Optimized Semantic Feature Extraction (HMOSFE), designed to enhance semantic feature extraction from English sentences. The proposed HMOSFE model comprises fusion of hierarchical clustering and fuzzy-based feature extraction, HMOSFE aims to capture intricate semantic relationships within sentences, providing nuanced insights into the underlying meaning of textual content. The model employs pre-trained word embeddings for term representation, calculates a similarity matrix using cosine similarity, and utilizes hierarchical clustering for document grouping. Fuzzy logic contributes to assigning weights to features, enabling a more refined understanding of semantic significance. The paper presents comprehensive results, including semantic similarity estimations, clustering distances, and fuzzy memberships, demonstrating the effectiveness of HMOSFE across diverse documents.