A Topic Recognition Method of News Text Based on Word Embedding Enhancement

Area of Science:

• Natural Language Processing
• Machine Learning
• Information Retrieval

Background:

• Topic recognition is crucial for online public opinion monitoring and news recommendation.
• Existing methods struggle to fully leverage semantic and syntactic features for improved accuracy.
• Combining topic models and word embeddings shows promise but lacks a standardized approach.

Discussion:

• This paper proposes a novel framework for news topic recognition using enhanced word embeddings.
• The framework integrates probabilistic topic models (LDA) with word embedding models (Word2vec, GloVe).
• It extracts and fuses topic distribution, semantic knowledge, and syntactic relationships for richer text representation.

Key Insights:

• The proposed method enhances text expressiveness and reduces dimensionality by utilizing document-topic relationships and contextual information.
• Experimental results on benchmark datasets (20NewsGroup, BBC News) demonstrate the framework's effectiveness.
• The approach offers a superior alternative to traditional topic recognition methods.

Outlook:

• Further research can explore advanced fusion techniques for topic models and word embeddings.
• The framework's adaptability to other text classification tasks warrants investigation.
• Real-world deployment in large-scale news recommendation systems is a potential future direction.