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Natural Language Processing with Java

Natural Language Processing with Java

By : Richard M. Reese
2 (3)
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Natural Language Processing with Java

Natural Language Processing with Java

2 (3)
By: Richard M. Reese

Overview of this book

Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes. You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more. By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.
Table of Contents (14 chapters)
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Inverse document frequency


If we consider all the terms with the same importance for all the queries, it will not work for all queries. If the documents are related to ice, it is obvious that "ice" will be in almost all documents, probably with high frequency. Collection frequency and document frequency are two different terms that need to be explained. A collection contains many documents. The collection frequency (cf) shows the frequency of terms (t) in all documents in the collection, whereas the document frequency (df) shows the frequency of t in a single document. So the word "ice" will have a high collection frequency, as it is presumed to appear in all the documents in the collection. A simple idea is to reduce the weight of such terms if they have a high collection frequency. Inverse frequency is defined as follows:

Here, is the total number of documents in a collection. The idf of a frequent term is likely to be low, and that of a rare term will be high.

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