Natural Language Processing (NLP) involves the use of algorithms and
models and various computational techniques to analyze, process, and generate natural
language data, including speech and text. NLP helps computers interact with humans in
a more natural way, which has become increasingly important as more humancomputer interactions take place. NLP allows machines to process and analyze
voluminous unstructured data, including social media posts, newspaper articles,
reviews from customers, emails, and others. It helps organizations extract insights,
automate tasks, and improve decision-making by enabling machines to understand and
generate human-like language. A linguistic background is essential for understanding
NLP. Linguistic theories and models help in developing NLU systems, as NLP
specialists need to understand the structure and rules of language. NLU systems are
organized into different components, including language modelling, parsing, and
semantic analysis. NLU systems may be assessed through the use of metrics that
includes measures like precision and recall, as well as indicators that convey
meaningful information that include F1 score and others. Semantics and knowledge
representation are central to NLU, as they involve understanding the meaning of words
and sentences and representing this information in a way that machines can use.
Approaches to knowledge representation include semantic networks, ontologies, and
vector embeddings. Language modelling is an essential step in NLP that sees usage in
applications like speech recognition, text generation, and text completion and also in
areas such as machine translation. Ambiguity Resolution remains a major challenge in
NLP, as language is often ambiguous and context-dependent. Some common
applications of NLP include sentiment analysis, chatbots, virtual assistants, machine
translation, speech recognition, text classification, text summarization, and information
extraction. In this chapter, we show the applicability of a popular unsupervised learning
technique, viz., clustering through K-Means. The efficiency provided by the K-Means
algorithm can be improved through the use of an optimization loop. The prospects for
NLP are promising, with an increasing demand for AI-powered language technologies
in various industries, including healthcare, finance, and e-commerce. There is also a
growing need for ethical and responsible AI systems that are transparent and
accountable.
Keywords: K-Means, Language analysis, Language modeling, Machine translation, NLU, NLP challenges, NLP approaches, Text clustering.
