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The Realm of Natural Language Processing: A Comprehensive Look at Essential Techniques

Updated: Aug 1

Introduction Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that concentrates on equipping computers with the ability to comprehend, interpret, and produce human language. NLP methodologies have experienced substantial progress over time, with numerous state-of-the-art developments emerging. This article delves into some of the most prominent NLP techniques, such as tokenization, stemming, lemmatization, part-of-speech (POS) tagging, named entity recognition (NER), sentiment analysis, and machine translation. Additionally, we will investigate the primary algorithms and models that have facilitated these techniques.

  1. Tokenization

Tokenization refers to the procedure of dividing text into individual words, phrases, or tokens. This serves as a crucial initial phase in NLP, as it enables the computer to scrutinize the text more effectively.

Reference: Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.

  1. Stemming and Lemmatization

Stemming and lemmatization are methods employed to condense words to their core form. While stemming involves eliminating affixes to acquire the stem, lemmatization uses a linguistic strategy to determine a word's base form.

Reference: Jurafsky, D., & Martin, J. H. (2019). Speech and Language Processing. Pearson.

  1. Part-of-Speech (POS) Tagging

POS tagging consists of assigning each word in a sentence to its respective part of speech, such as noun, verb, adjective, and so on. This process assists in comprehending the syntactic structure of a sentence and is vital for various NLP tasks (Jurafsky & Martin, 2019).

  1. Named Entity Recognition (NER)

NER is a method that identifies and categorizes named entities like individuals, organizations, and locations within a given text. NER is crucial for information extraction and text summarization tasks.

Reference: Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), 3-26.

  1. Sentiment Analysis

Sentiment analysis strives to ascertain the sentiment or emotion conveyed in a text. This technique is extensively utilized for evaluating user reviews, social media content, and customer feedback to acquire insights into consumer opinions.

Reference: Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.

  1. Machine Translation

Machine translation entails the automated translation of text from one language to another. Recent breakthroughs in NLP, such as the inception of the Transformer architecture (Vaswani et al., 2017) and models like BERT (Devlin et al., 2018) and GPT-3 (Brown et al., 2020), have substantially enhanced the quality and accuracy of machine translation.


Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training

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