A Brief Overview of NLP

In this article you will learn about history of natural language processing, what is meant by NLP and a common task of analyzing text, as well as applications of natural language processing.

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Introduction

Natural language processing (NLP) is a branch of artificial intelligence (AI) that involves the use of computational linguistics to process and analyze human language. The history of NLP can be rooted several years back, between 1950s-1960s when Alan Turing introduced the idea of “Universal Machine” in which a computer can be used to imitate human conversation as if people were talking.

Natural Language Processing

Natural language processing refers to the ability of computer programs to understand human language when its spoken and written. It’s a subfield of artificial intelligence and computer science that enable computers to understand, interpret, and manipulate human languages.

Common Techniques of Analyzing Text in NLP

1. Tokenization is the process of splitting sentence into characters, words or phrase level.
2. Embeddings refers to the conversion of text data to numerical values.
3. Parsing and part-of-speech is the process of tagging a word as a part of speech after being tokenized and recognize the relationship between words. Example: “The boy killed the dog” can be POS tagged as, boy=noun, killed=verb, dog=noun.
4. Word and Phrase Frequencies is a useful way of analyzing a large text by identifying how many times a word or phrase of interest appears in a text. Example: “School is where learning takes place, without school there is no learning.” The phrase “School” have a frequency of score 2, and “learning” also have frequency of 2 score.
5. N-grams is a way of splitting text into a sequence of words with a certain length. Example: “The boy killed the dog.” can be represented as 2 words n-grams. “The boy”, “boy killed”, “killed the”, “the dog.”
6. Noun phrase extraction is an NLP common task used to understand the meaning of a sentence. Noun phrase extraction is done by identifying subject, or object having “a” or “n” or “the” preceding it.
7. Inflection is an NLP common task that enable you to take a word and get the singular or plural of the word.

Application of NLP

Here are some few applications of natural language processing NLP:

1. Sentiment Analysis: NLP techniques are used to analyze and determine the sentiment or opinion expressed in text data, such as customer reviews, social media posts, or survey responses. This helps businesses gauge public opinion, monitor brand reputation, and make informed decisions.
2. Text Classification: NLP enables the classification of text documents into predefined categories or classes. This is used in various applications such as spam detection, topic classification, news categorization, sentiment analysis, and language identification.
3. Machine Translation: NLP plays a significant role in developing machine translation systems that automatically translate text from one language to another. These systems utilize linguistic analysis, statistical models, and neural networks to improve translation accuracy and fluency.
4. Information Extraction: NLP enables the extraction of structured information from unstructured text data. This includes tasks such as extracting entities, relationships, events, and facts from documents, enabling the organization and retrieval of valuable information.
5. Text Generation: NLP is used to generate human-like text, such as generating product descriptions, writing news articles, or creating creative content. Language models and generation algorithms are employed to produce coherent and contextually relevant text.

Conclusion

Natural language processing NLP is a subcategory of artificial intelligence that enable computers to interact with human in natural language. it was proposed by Alan Turing in 1950s and its applicable in sentiment analysis, information extraction, text generation and so on.