In this chapter, we introduced a number of NLP tasks and showed how they are supported. In particular, we used OpenNLP and DL4J to illustrate how they are performed. While there are a number of other libraries available, these examples provide a good introduction to the techniques.

We started with an introduction to basic NLP terms and concepts such as named entity recognition, POS, and relationships between elements of a sentence. Named entity recognition is concerned with finding and labeling the parts of a sentence such as people, locations, and things. POS associates labels with elements of a sentence. For example, NN refers to a noun and VB to a verb.

We then included a discussion of the Word2Vec and Doc2Vec neural networks. These were used to classify text, both with labels and by similarity with other words. We demonstrated the use of DL4J resources to create feature vectors for document association with labels.

While the identification of these associations is interesting, a more useful analysis is performed when relationships are extracted from a sentence. We demonstrated how relationships are found using OpenNLP. The POS are associated with each word and the relationships between the words are shown using a set of tags and parentheses. This type of analysis can be used for more sophisticated analyses such as language translation and grammar checking.

Finally, we discussed and showed examples of sentiment analysis. This process involves classifying text based on its tone or contextual meaning. We examined a process for classifying movie reviews as positive or negative.

In this chapter, we demonstrated various techniques for text analysis and classification. In our next chapter, we will examine techniques designed for video and audio analysis.