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Getting started with NLP (Library Overview)

by Sri Balaji J

Natural Language Processing (NLP) is a broad subject that falls under the Artificial Intelligence (AI) domain. NLP allows computers to interpret text and spoken language in the same way that people do. NLP must be able to grasp not only words, but also phrases and paragraphs in their context based on syntax, grammar, and other factors. NLP algorithms break down human speech into machine-understandable fragments that can be utilized to create NLP-based software.
Because of the development of useful NLP libraries, NLP is now finding applications across a wide range of industries. NLP has become a critical component of Deep Learning development. Among other NLP applications, extracting useful information from text is crucial for building chatbots and virtual assistants, among other NLP applications, because training NLP algorithms require a large amount of data for better performance, but our Google Assistant and Alexa are becoming more natural by the day. Here are some basic libraries to get started with NLP.

NLTK Natural Language Toolkit is one of the most frequently used libraries in the industry for building Python applications that interact with human language data. NLTK can assist you with anything from splitting sentences from paragraphs to recognizing the part of speech of specific phrases to emphasizing the primary theme. It is a highly important tool for preparing text for future analysis, such as when using Models. It assists in the translation of words into numbers, with which the model may subsequently function. This collection contains nearly all of the tools required for NLP. It helps with text classification, tokenization, parsing, part-of-speech tagging and stemming. spaCy spaCy is a python library built for sophisticated Natural Language Processing. It is based on cutting-edge research and was intended from the start to be utilized in real-world products. spaCy has pre-trained pipelines and presently supports tokenization and training for more than 60 languages. It includes cutting-edge speed and neural network models for tagging, parsing, named entity identification, text classification, and other tasks, as well as a production-ready training system and simple model packaging, deployment, and workflow management. Gensim Gensim is a well-known Python package for doing natural language processing tasks. It has a unique feature that uses vector space modeling and topic modeling tools to determine the semantic similarity between two documents.

CoreNLP CoreNLP can be used to create linguistic annotations for text, such as Token and sentence boundaries, Parts of speech, Named entities, Numeric and temporal values, dependency and constituency parser, Sentiment, Quotation attributions, and Relations between words. CoreNLP supports a variety of Human languages such as Arabic, Chinese, English, French, German, and Spanish. It is written in Java but has support for Python as well. Pattern Pattern is a python based NLP library that provides features such as part-of-speech tagging, sentiment analysis, and vector space modeling. It offers support for Twitter and Facebook APIs, a DOM parser, and a web crawler. Pattern is often used to convert HTML data to plain text and resolve spelling mistakes in textual data. Polyglot Polyglot library provides an impressive breadth of analysis and covers a wide range of languages. Polyglot's SpaCy-like efficiency and ease of use make it an excellent choice for projects that need a language that SpaCy does not support. The polyglot package provides a command-line interface as well as library access through pipeline methods.

TextBlob TextBlob is a python library that is often used for natural language processing (NLP) tasks such as voice tagging, noun phrase extraction, sentiment analysis, and classification. This library is based on the NLTK library. Its user-friendly interface provides access to basic NLP tasks such as sentiment analysis, word extraction, parsing, and many more. Flair Flair supports an increasing number of languages, you may apply the latest NLP models to your text, such as named entity recognition, part-of-speech tagging, and classification, as well as sense disambiguation and classification. It is a deep learning library built on top of PyTorch for NLP tasks. Flair natively provides pre-trained models for NLP tasks such asText classification, Part-of-Speech tagging and Name Entity Recognition

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