Natural Language Processing with Python Cookbook
Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora.
This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques.
By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python.
|Course Length||9 hours 28 minutes|
|Date Of Publication||24 Nov 2017|
|Tokenization – learning to use the inbuilt tokenizers of NLTK|
|Stemming – learning to use the inbuilt stemmers of NLTK|
|Lemmatization – learning to use the WordnetLemmatizer of NLTK|
|Stopwords – learning to use the stopwords corpus and seeing the difference it can make|
|Edit distance – writing your own algorithm to find edit distance between two strings|
|Processing two short stories and extracting the common vocabulary between two of them|
|Regular expression – learning to use *, +, and ?|
|Regular expression – learning to use $ and ^, and the non-start and non-end of a word|
|Searching multiple literal strings and substring occurrences|
|Learning to create date regex and a set of characters or ranges of character|
|Find all five-character words and make abbreviations in some sentences|
|Learning to write your own regex tokenizer|
|Learning to write your own regex stemmer|