Resultaten voor 'deepti chopra'

1. Deepti Chopra

Building Machine Learning Systems Using Python

1. Deepti , Chopra

Named Entity Recognition in Natural languages using Hidden Markov Mode

Named Entity Recognition (NER) aims to detect and categorize named entities in a document into certain predefined named entities classes such as Name of Person, Location, Organization, River, Location, Expressions of times, Quantities, Monetary value, Percentages, etc. In the nomenclature of computational linguistics tasks, Named Entity Recognition lies in the domain of "information extraction", which detect definite kinds of information from documents as opposed to the more general task of "document management" which seeks to extract all of the information found in a document Named Entity Recognition in Indian Languages is a very problematical task. Since, In English and in Other European Languages, the method of Capitalization makes it easy to identify the Proper Nouns in a document, which is not so in case of Indian Languages. Moreover, Indian languages are free order, and highly inflectional and morphologically rich in nature. I have performed NER using HMM in following Natural Languages: Hindi, Bengali, Telugu, Urdu, Marathi, Punjabi, English and French. I have also dealt with problem of Unknown words in Named Entity Recognition using Transliteration.

1. Jacob , Perkins
2. Nitin , Hardeniya
3. Deepti , Chopra

Natural Language Processing

Learn to build expert NLP and machine learning projects using NLTK and other Python librariesKey Features:- Break text down into its component parts for spelling correction, feature extraction, and phrase transformation- Work through NLP concepts with simple and easy-to-follow programming recipes- Gain insights into the current and budding research topics of NLP Book Description:Natural Language Processing is a field of computational linguistics and artificial intelligence that deals 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. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages.The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy.The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods.The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products:- NTLK essentials by Nitin Hardeniya- Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins- Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti MathurWhat You Will Learn:- The scope of natural language complexity and how they are processed by machines- Clean and wrangle text using tokenization and chunking to help you process data better- Tokenize text into sentences and sentences into words- Classify text and perform sentiment analysis- Implement string matching algorithms and normalization techniques- Understand and implement the concepts of information retrieval and text summarization- Find out how to implement various NLP tasks in PythonWho this book is for:If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable.