Word associations provide valuable insights into linguistic patterns, semantic relationships, and syntactic structures. This study examines the approach described in the seminal paper “Word Association Norms, Mutual Information, and Lexicography” by Church and Hanks (1989). The primary focus is on using the association ratio to identify significant word pair associations and applying clustering techniques to organize these associations into meaningful groups.
Objective
This case study aims to:
Compute Association Ratios (AR) for word pairs using co-occurrence data.
Construct a similarity matrix based on AR scores.
Use hierarchical clustering to group related words and visualize their relationships.
Dataset
The analysis uses a synthetic text corpus inspired by real-world contexts. The corpus contains sentences describing descriptive and contextual associations of words like “bright,” “light,” “smart,” and “student.”
Programming (Mobile, Web, Database and Machine Learning). Learned life lessons at the Center for Artificial Intelligence Technology (CAIT), FTSM, UKM, Malaysia.
