Different Approach for Induction of Unsupervised Lexical Semantic Frames

The challenge of dealing with the inherent ambiguity and constant evolution of human language could be addressed through semantic role labeling, a process of assigning labels to words or phrases to indicate their roles in a sentence. The goal is to discern sentence meanings by detecting and assigning specific roles to arguments associated with predicates or verbs. This paper emphasizes the impact of lexicons, particularly VerbNet and FrameNet, on labeling and classification. To overcome language ambiguity, we look into solving two tasks: grouping verbs into frame type clusters and clustering verb arguments into frame-specific slots or generic roles. The research effort described herein employs a fully unsupervised approach, utilizing Agglomerative clustering and extracting information from the CoNLL-U format. Three models — BERT, ELMo, and Word2Vec — generate embeddings, and the results are analyzed through agglomerative clustering with optimized hyperparameters. The paper suggests potential enhancements, such as incorporating a small set of annotated data for semi-supervised learning, expanding the dataset, and assessing system performance across different languages by training language models on new language samples. Overall, the research strives to provide a comprehensive solution to the multifaceted challenges of understanding and interpreting evolving human language.