Natural Language Processing Training at Massive Tech –Natural language processing is one of the technologies that drive Artificial Intelligence. Its core functionally to allow machines to understand human speech. Technologies such as Google Assistant and Alexa use NLP to translate our words into text, that is then decoded by a complex set of algorithms which can be understood by machines. With the help of NLP, it is possible to create intelligent and intuitive machines that can communicate with us.
The ultimate objective of NLP is to read, decipher, understand, and make sense of the human language in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages.
Prerequisites
- Phython Programming
Key Features
- LIVE Instructor-led Classes
- 24x7 on-demand technical support for assignments, queries, quizzes, project, etc.
- Flexibility to attend the class at your convenient time.
- Server Access to Massive's Tech Management System until you get into your dream carrier.
- A huge database of Interview Questions
- Professional Resume Preparation
- Earn a Skill Certificate
- Enroll today and get the advantage.
Curriculum
- Introduction to Regular Expressions
- Tokenization of text
- Normalization of text
- Substituting and correcting tokens
- Applying Zipf’s law to text
- Applying similarity measures using the Edit Distance Algorithm
- Applying Similarity measures using Jaccard’s Coefficient
Applying similarity measures using Smith Waterman
- Understanding word frequency
- Applying smoothing on the MLE model
- Develop a backup mechanism for MLE
- Data Interpolation
- Language modelling using metropolis hastings
- Gibbs sampling in language processing
- Introducing Morphology
- Understanding Stemmer
- Lemmatization
- Morphological analyzer
- Morphological generator
- Introducing Parsing
- Treebank construction
- Extracting Context Free Grammer (CFG) rules from Treebank
- CYK chart parsing algorithm
- Earley chart parsing algorithm
- Introducing semantic analysis
- Named-entity recognition (NER)
- NER system using the HMM
- Training NER using machine learning toolkits
- NER using POS tagging
- Generation of the synset id from Wordnet
- Disambiguating senses using Wordnet
- Introducing sentiment analysis
- Sentiment analysis using NER
- Sentiment analysis using machine learning
- Evaluation of the NER system
- Introducing information retrieval
- Stop word removal
- Information retrieval using a vector space model
- Vector space scoring and query operator interactions
- Text summarization
- Introducing discourse analysis
- Discourse analysis using Centering Theory
- Anaphora resolution
- The need for the evaluation of NLP Systems
- Evaluation of IR systems
- Metrics for error identification
- Metrics based on lexical matching
- Metrics based on Syntactic matching
- Metrics using shallow semantic matching
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