Early approaches in NLP

Before the widespread use of neural networks (NNs) in language processing, NLP was largely grounded in methods that counted words. Two particularly notable techniques were count vectors and Term Frequency-Inverse Document Frequency (TF-IDF). In essence, count vectors tallied up how often each word appeared in a document. Building on this, Dadgar et al. applied the TF-IDF algorithm (historically used for information retrieval) to text classification in 2016. This method assigned weights to words based on their significance in one document relative to their occurrence across a collection of documents. These count-based methods were successful for tasks such as searching and categorizing. However, they presented a key limitation in that they could not capture the semantic relationships between words, meaning they could not interpret the nuanced meanings of words in context. This challenge paved the way for exploring NNs, offering a deeper and more nuanced way to...