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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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Why is NER difficult?


Like many NLP tasks, NER is not always simple. Although the tokenization of a text will reveal its components, understanding what they are can be difficult. Using proper nouns will not always work because of the ambiguity of language. For example, Penny and Faith, while valid names, may also be used for a measurement of currency and a belief, respectively. We can also find words such as Georgia that are used as the name of a country, a state, and a person. We can also not make a list of all people or places or entities as they are not predefined. Consider the following two simple sentences:

  • Jobs are harder to find nowadays
  • Jobs said dots will always connect

In these two sentences, jobs seems to be the entity but they are not related, and in second sentence it's not even an entity. We need to use some complex techniques to check for the occurrence of entities in the context. Sentences may use the same entity's name in different ways. Say, for example, IBM and International...

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