English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 37m | 335 MB
Explore various NLP tasks while enhancing your Python skills in real-world scenarios
Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.This course will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK. You will sequentially be guided through applying machine learning tools to develop various models. We’ll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Text Summarization, and Anaphora Resolution.
This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.
What You Will Learn
- Implement string matching algorithms and normalization techniques
- Implement statistical language modeling techniques
- Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
- Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
- Develop an NER-based system and understand and apply the concepts of semantic analysis
- Understand and implement the concepts of Information Retrieval and text summarization
- Develop a Discourse Analysis System and Anaphora Resolution based system
Table of Contents
Working with Strings
1 The Course Overview
2 Tokenization
3 Normalization
4 Substituting and Correcting Tokens
5 Similarity Measures
6 Understanding Word Frequency
Parts-of-Speech Tagging – Identifying Words
7 Introducing Parts-of-Speech Tagging
8 Default Tagging
9 Statistical Modeling Involving the n-gram Approach
10 Developing a Chunker Using POS-tagged Corpora
Parsing – Analyzing Training Data
11 Treebank Construction
12 Extracting Context Free Grammar (CFG) Rules from Treebank
13 Creating a Probabilistic Context Free Grammar from CFG
Semantic Analysis – Meaning Matters
14 Introducing Semantic Analysis
15 Introducing NER
16 An NER System Using Hidden Markov Model
17 Generation of the synset id from Wordnet
Discourse Analysis and Evaluation of NLP Systems
18 Introducing Discourse Analysis
19 Anaphora Resolution
20 Evaluation of NLP Tools
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