5th Sep, 2023 | Aarav P.
In this era of technology, natural language processing (NLP) plays a role in advanced innovations. It is reshaping our computer interactions and revolutionizing the world of data analysis. This blog delves into the world of NLP techniques, unveiling the magic behind the algorithms that enable machines to comprehend, process, and generate human language.
NLP or natural language processing, falls under the umbrella of artificial intelligence. It allows computers to comprehend, interpret, and engage with language in a manner that resembles communication. By leveraging machine learning algorithms and linguistic principles, NLP empowers computers to process, analyze, and generate human language, making it an indispensable tool in various industries.
From sentiment analysis to machine translation, NLP techniques have transformed industries and revolutionized our interactions with technology. In this section, we unveil the top 7 NLP techniques that every data enthusiast, developer, and language aficionado should know.
Sentiment analysis, also known as opinion mining, is a method in natural language processing (NLP) that employs algorithms to detect and ascertain the sentiment or emotion conveyed within a given text. Sentiment analysis aims to understand whether the text conveys a positive, negative, or neutral sentiment.
In essence, sentiment analysis aims to mimic human's ability to comprehend emotions conveyed through language. By examining and categorizing the emotions conveyed in information, sentiment analysis can offer insights into people's perceptions and attitudes towards specific products, services, brands, events, or any other topic of significance.
This instance showcased on the Revuze platform demonstrates how customers evaluate distinct aspects of the product.
Named entity recognition (NER) aims to identify and categorize named entities in a given text. The primary objective of named entity recognition is to locate and categorize these entities to extract relevant information and understand the context of a given text. By automatically recognizing named entities, NER helps NLP models comprehend the significance and relationships between different elements in a document.
An illustration from Towards Data Science demonstrates the practical application of this concept. In the given example, the Named Entity Recognition API accurately identifies all the pertinent tags within the article, making it a valuable tool for effective categorization purposes.
Text summarization, also called automated summarization, involves applying natural language processing (NLP) methods to condense a given text while preserving its details. The objective of text summarization is to offer a coherent version of the text, allowing readers to capture the main points and core ideas without going through the entire content.
Text summarization is a challenging task in NLP, requiring advanced algorithms and models to understand the context, relevance, and coherence of the text. It involves techniques such as natural language understanding, information extraction, and content generation. Modern approaches to text summarization often involve leveraging machine learning, deep learning, and transformer-based models to achieve more accurate and contextually aware summaries.
As an instance, the image displayed below portrays an article that underwent processing through a machine learning algorithm to produce a summary.
Topic modeling is a method in natural language processing (NLP) that seeks to uncover the themes or subjects within a set of written materials. It is an unsupervised machine learning method that analyzes the words and patterns within the text data to identify underlying topics without any prior knowledge of the topics themselves.
The goal of topic modeling is to represent each document in the dataset as a combination of different topics, where each topic is a distribution of words. It allows us to gain insights into the main themes present in the text corpus, facilitating better organization and understanding of large volumes of textual data. Check out the image from Scaler below to understand text modeling:
Text classification involves the assignment of categories or labels to a given text. The primary goal of text classification is to sort documents into defined categories according to their content and unique characteristics.
In text classification, the input is a set of textual data, and the output is the assigned category or label for each document. The process involves training a machine learning model using a labelled dataset, where each document is associated with its corresponding category.
Keyword extraction is a technique to identify and retrieve words or phrases from a given text. The objective of keyword extraction is to identify the meaningful terms in the text that represent the central ideas or subjects discussed in the document.
Keyword extraction is distinct from text classification and topic modeling. While text classification assigns predefined categories to documents, and topic modeling uncovers latent topics within a corpus, keyword extraction focuses on extracting specific words or phrases that are highly informative and representative of the document's content.
Lemmatization and stemming are both NLP techniques used to reduce words to their base or root forms, making them more manageable for text analysis and processing.
Stemming is a linguistic process in which words are reduced to their word stems. These stems do not always correspond to the morphological roots found in dictionaries; rather, they reflect equal or smaller forms of the original words.
Stemming algorithms use a rule-based approach and are similar to heuristic methods that truncate the ends of words. When a word is examined by these algorithms, a set of conditionals is used to identify the best approach to reduce it.
Lemmatization, conversely, is a more advanced NLP technique that uses a vocabulary or a lexicon to convert words into their base or dictionary forms, called lemmas. The lemmatized word is a valid word that represents the base meaning of the original word. Lemmatization considers the part of speech (POS) of the word and ensures that the output is a proper word in the language.
In summary, stemming is a faster and simpler technique that truncates words to their stems, while lemmatization is more accurate and involves converting words to their valid lemmas. The choice between stemming and lemmatization depends on the specific requirements of the NLP task, the language being used, and the desired level of accuracy.
This image from itnext illustrates the contrast in the functioning of stemming and lemmatization, employing various forms of the word "change".
As NLP advances rapidly, its integration into various industries is becoming increasingly prevalent. From improving customer interactions through chatbots and virtual assistants to enabling sophisticated data analysis in healthcare and finance, NLP techniques propel the next wave of innovation.
By understanding the fundamentals of NLP and embracing its vast potential, businesses and individuals can unlock new opportunities for growth, efficiency, and enhanced user experiences. With the advancement of NLP techniques, our ability to utilize the potential of language and connect humans with machines will also progress.
Whether you have expertise in data science, a passion for languages, or aspirations in the tech industry, exploring the realm of natural language processing (NLP) is bound to unlock a universe where human communication and machine intelligence come together. This synergy paves the way for possibilities that are solely defined by our creativity and imagination.
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