Last updated on April 11th, 2024 at 08:46 am
NLP, also known as Natural Language Processing, plays an important role in various applications and technologies that involve human interaction, such as chatbots, virtual assistants, sentiment analysis, and text summarisation, among many others.
One of the main reasons why natural language processing is extremely crucial in today’s world is it helps to bridge the gap between human language and computational systems.
NLP is a rapidly growing field, and its application can be witnessed across various industries, such as education, customer service and e-commerce. The various advancements in NLP have ultimately made it possible for computers to properly and accurately understand and analyse human languages.
On that note, here is a detailed guide to everything there is to know about NLP and its various components.
What Is Natural Language Processing?
Natural language processing can be described as a field of study and technology that primarily focuses on enabling computers to understand, interpret and process human language in a meaningful way. It involves the development and application of algorithms, models and techniques that enable computers to analyse and respond to human language in the desired manner.
To achieve the same, it performs a wide variety of tasks, such as text classification, sentiment analysis, information extraction, and language translation. The ultimate goal of NLP is to process and understand human language in such a manner that is similar to how humans do.
Steps Involved In Natural Language Processing
Mentioned below is a step-by-step guide to how NLP actually operates. Please note that this is just a generalised view of the NLP steps, as they can differ based on the specific task or the complexity of the problem.
Sentence Segmentation
The first and foremost step in NLP is sentence segmentation, wherein an entire paragraph is divided into multiple sentences. It facilitates a better understanding of the overall text.
Word Tokenisation
Tokenisation refers to the process of breaking down sentences into separate words or tokens. It forms the basis for further analysis and allows the computer to understand the structure of the text.
Stemming
Stemming refers to the process of simplifying text analysis and improving computational efficiency by reducing different forms of words to a common base form. Simply put, it helps in preprocessing text. With the help of this technique, you can treat different variations of the same word as a single entity. For example, intelligently, intelligence, and intelligent.
Lemmatisation
Lemmatisation is another technique used in NLP that is responsible for reducing words to their base or dictionary form, which we refer to as the lemma. It shares some similarities with stemming, the only difference being lemma is an actual word. By converting different words to their lemmas, you can consolidate different variations of a word into a single representation. This, in turn, enables better analysis, interpretation and comparison of text data.
Stop Word Analysis
In the English language, there are various words used that do not necessarily carry any significant meaning or contribute much to the overall understanding of the text. A few examples of the same include ‘a', ‘the’, ‘and’, ‘or’, and ‘an’, among others. This is where stop-word analysis comes into play. It effectively identifies and filters them out accordingly. In this manner, it helps to increase the efficiency and effectiveness of text processing and analysis tasks.
Dependency Parsing
The ultimate goal of dependency parsing is to analyse and represent the grammatical structure and relationships between each word of a sentence. It typically involves creating a tree and assigning a single word as the parent word. The root node will be the main verb of the sentence. With the help of dependency parsing, we can indicate the syntactic roles and dependencies such as subject, object, or conjunction between the words.
Part-Of-Speech Tagging
Part-of-speech POS tagging, also often referred to as POS annotation, is a very important step in natural language processing. It involves assigning grammatical categories or labels to words in a sentence. Each word is tagged with a distinct part-of-speech tag that represents its grammatical category within the sentence.
Applications Of Natural Language Processing
Some of the many applications of NLP include,
Text Summarisation
Text summarisation typically involves categorising or assigning labels to text documents based on their content. It is especially useful in tasks such as topic classification, document classification, or sentiment classification.
Sentiment Analysis
Also referred to as opinion mining, sentiment analysis aims to determine the particular sentiment of a text and categorise the same into different types, such as positive, negative or neutral. It is extremely useful, especially for tasks involving social media monitoring or customer feedback analysis.
Question Answering
Question answering aims to develop systems that can accurately understand and answer questions that are posed in human language. For the same, it uses various techniques, such as logical reasoning, text comprehension, and information retrieval.
Chatbots and Virtual Assistants
NLP is also crucial for the development of conversational agents such as chatbots and virtual assistants. They help to understand the user queries, generate necessary responses, and simulate a human-like conversation.
Named Entity Recognition
Named Entity Recognition, also known as NER, helps to identify and classify named entities such as locations, dates, and names of individuals and organisations in a text. In this manner, you can extract relevant information from unstructured text data in a hassle-free manner.
Conclusion
Hopefully, with this, you have a clear understanding of natural language processing and its various important aspects. From information extraction and fraud detection to machine translation, and speech recognition, NLP performs a wide range of tasks. It empowers organisations to extract valuable insights from text data, enhance communication, and ultimately provide quality service to users.
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