Contextualized Language Model

Contextualized language models (CLMs) aim to understand the meaning of words and sentences by considering their surrounding context, going beyond simple word-to-vector mappings. Current research focuses on improving CLM performance in various tasks, including topic modeling (using graph neural networks to capture word correlations), software vulnerability detection (combining CLMs with graph convolutional networks), and social media analysis (leveraging CLMs for improved text classification). These advancements are significantly impacting fields like natural language processing, software engineering, and social sciences by enabling more accurate and nuanced analysis of textual data.