Some of the applications of NLG are question answering and text summarization.

What is text in natural language. Although it may sound similar text mining is very different from the web search version of search that most of us are used to involves serving already known information to a user. As we will see the annotation schemas discussed earlier for example provide rich starting points as the input data source for the ML process the training phase. In the general framework of knowledge discovery data mining techniques are usually dedicated to information extraction from structured databases.

It provides easy-to-use interfaces to many corpora and lexical resources. A text is an extended structure of syntactic units i. Natural language understanding is a branch of artificial intelligence that uses computer software to understand input in the form of sentences using text or speech.

Sentiment Analysis is a natural language processing technique for text analytic which are used to analyze the polarity of a document or a sentence or an attribute. NLG is much simpler to accomplish. First the NLP system identifies what data should be converted to text.

Text mining techniques on the other hand are dedicated to information extraction from unstructured textual data. For our purposes the data that an ML algorithm encounters is natural language most often in the form of text and typically annotated with tags that highlight the specific features that are relevant to the learning task. Text as super-sentence such as words groups and clauses and textual units that is marked by both coherence among the elements and completion.

Natural Language Generation NLG is a subfield of NLP designed to build computer systems or applications that can automatically produce all kinds of texts in natural language by using a semantic representation as input. Text mining plays an important role to extract meaningful information from the data by identifying and exploring the interesting patterns. By breaking up the text into small known fragments we can apply a small ish set of rules to combine them into some larger meaning.

This makes it easy to compare texts of differing lengths. Programming languages work by breaking up raw code into tokens and then combining them by some logic the programs grammar in natural language processing. If you asked the computer a question about the weather it most likely did an online search to find your answer and from there it decides.