Introduction to NLP

3.5.1.1. Introduction to NLP#

3.5.1.1.1. Introduction#

NLP role:

  • Facilitates Communication: NLP enables seamless interaction between humans and computers: powering chatbots, virtual assistants, and machine translation systems.

  • Extracts Meaningful Information: NLP helps extract insights from unstructured text data: sentiment analysis, named entity recognition, and text summarization.

  • Derives Insights: NLP algorithms analyze textual data to derive patterns and insights, valuable for tasks like market research, social media analysis, and customer feedback analysis.

  • Automates Tasks: NLP automates language-related tasks such as answering queries, categorizing documents, and generating reports, enhancing efficiency and accuracy.

  • Personalizes Experiences: NLP enables personalized recommendations, content filtering, and targeted advertising by understanding user preferences and behaviors from their language usage.

Important tasks of NLP:

  • Text Classification

  • Text Matching / Similarity:

    • Levenshtein Distance

    • Phonetic Matching

    • Flexible String Matching

    • Cosine Similarity

  • Coreference Resolution

  • Text Summarization

  • Machine Translation

  • Natural Language Generation and Understanding

    • Chatbot

  • Optical Character Recognition

  • Document to Information

    • Survey Analysis

Important Libraries for NLP (python)

  • Scikit-learn: Machine learning in Python

  • Natural Language Toolkit (NLTK): The complete toolkit for all NLP techniques.

  • Pattern – A web mining module for the with tools for NLP and machine learning.

  • TextBlob – Easy to use nlp tools API, built on top of NLTK and Pattern.

  • spaCy – Industrial strength NLP with Python and Cython.

  • Gensim – Topic Modelling for Humans

  • Stanford Core NLP – NLP services and packages by Stanford NLP Group.

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