Word Boundary

Word boundary detection, the task of identifying the limits of words in continuous speech or text, is crucial for various natural language processing (NLP) applications. Current research focuses on developing unsupervised methods, often employing self-supervised learning and leveraging word embeddings or subword tokenization, to overcome limitations of lexicon-dependent approaches and improve efficiency and scalability across multiple languages. These efforts utilize diverse model architectures, including transformer-based language models and dynamic programming algorithms, aiming to accurately identify word boundaries even in the absence of explicit markers. Improved word boundary detection has significant implications for speech recognition, language modeling, and other NLP tasks, particularly in low-resource language settings.