Visual Exploration of Semantic Relationships in Neural Word Embeddings.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Data Visualization

Background:

• Neural language models generate distributed word representations (word embeddings) crucial for NLP tasks.
• Current visualization techniques like t-SNE and PCA offer limited insights into the complex structure of word embedding spaces.
• Existing methods struggle with domain-specific visualization challenges, hindering the understanding of semantic and syntactic relationships.

Purpose of the Study:

• To develop novel embedding techniques for visualizing semantic and syntactic analogies in word representations.
• To introduce methods for assessing the structural validity of these visualizations.
• To enhance the interpretability of word embedding spaces by incorporating uncertainty information.

Main Methods:

• Introduction of new embedding techniques tailored for analogy visualization.
• Development of statistical tests to validate the captured structures in embeddings.
• Augmentation of t-distributed stochastic neighbor embeddings (t-SNE) with uncertainty quantification.
• Proposal of two novel visualization views for comprehensive analogy analysis.

Main Results:

• The proposed techniques effectively visualize semantic and syntactic analogies, surpassing the limitations of t-SNE and PCA.
• New methods provide reliable interpretation of word embedding structures by conveying uncertainty.
• The introduced visualization views facilitate a deeper understanding of complex relationships within word spaces.

Conclusions:

• The novel embedding and visualization techniques offer significant improvements for analyzing word representations in NLP.
• These methods address critical domain-specific challenges, enabling more accurate insights into semantic and syntactic relationships.
• The augmented t-SNE embeddings and new views provide a robust framework for exploring and interpreting word embedding spaces.