A winning synergy – the coming together of NLP and ML

ML is a subfield of AI that involves training algorithms to learn from data, allowing them to make predictions or decisions without those being explicitly programmed. ML is driving advancements in so many different fields, such as computer vision, voice recognition, and, of course, NLP.

Diving a little more into the specific techniques of ML, a particular technique used in NLP is statistical language modeling, which involves training algorithms on large text corpora to predict the likelihood of a given sequence of words. This is used in a wide range of applications, such as speech recognition, machine translation, and text generation.

Another essential technique is DL, which is a subfield of ML that involves training artificial neural networks on large amounts of data. DL models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been shown to be adequate for NLP tasks such as language understanding, text summarization, and sentiment analysis.

Figure 1.2 portrays the relationship between AI, ML, DL, and NLP:

Figure 1.2 – The relationship between the different disciplines