Text Processing in NLP

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Introduction:

NLP is a field of Artificial Intelligence (AI) that allows computers to understand, interpret, and utilize human language in context. The reason human language is so complicated is primarily due to grammar, emotions, context words and terminologies of a particular field as well as different writing styles. The rise of Natural language processing:NLP enables machines to dissect this language and transmute unstructured text into structured data, which is easily consumable by computers.

Body:

Natural Language Processing employs a number of textual preprocessing methodologies including tokenization, lemmatization, vectorization and similarity modeling. These techniques are used to split the text into smaller pieces (such as words or sentences), extract root words, convert that text into a numerical vector and compare meaning between two sentences/sentences/documents. Further, after the data is processed it can feed machine learning and deep learning models for intelligent decision making and predictions.

Tokenization :

Tokenization breaks down a given sentence or text into smaller parts called tokens. Computers cannot read human sentence as a whole. Hence, first NLP divides the text into smaller chunks for processing.

Example:

Input: “NLP is very interesting”

Output : ["NLP", "is", "very", "interesting"]

Lemmatization

Lemmatization is the process of converting a word to its base or root form known as a lemma. This allows NLP systems to comprehend that different variants of a term signify the same thing.

Input :["running", "studies", "better"]

Output : ["run", "study", "good"]

Vectorization

Vectorization is the process of converting text or words into numerical form (vectors) so that machine learning models can understand and process them.TF-IDF Vectorization is an NLP technique used to convert text into numerical vectors based on the importance of words in a document. TF-IDF helps identify which words are important and meaningful in a sentence or document

Input

["I love NLP", "NLP is interesting"]

Output : 

[

 [1, 1, 1, 0],

 [0, 1, 0, 1]

]

Find similarity:

In NLP, finding similarity is the task of two texts to see how similar they are.It teaches computers to understand when two sentences are saying the same thing.Word Embeddings, Cosine Similarity and TF-IDF are commonly used techniques. Typically, the similarity score is between 0 and 1.

Input

Sentence 1: "I love NLP"

Sentence 2: "I like NLP"

Output

0.82

Summary: 

NLP is widely used these days in chatbots, virtual assistants, search engines, translation systems, sentiment analysis, recommendation systems, spam filtering, and AI-powered enterprise applications. Popular applications include Siri, Alexa, Google Translate, ChatGPT and customer service bots. NLP is a key factor in enhancing human-computer interaction and automating language-based tasks in different sectors.