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Natural Language Processing Case Study

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Chapter 1

INTRODUCTION

In this chapter, we provide a brief insight into Google Speech-to-text API which we have used in our project. Further we will be discussing about Natural Language Processing (NLP) and its applications.

1.1 Significance of the Google API

Google's Cloud Speech API will allow developers to convert audio to text within their own apps. The offering from Google will bring its neural network smarts to apps large and small, and opens up a wide range of interesting new possibilities.

The API includes a number of key functions. The automatic speech recognition is powered by learning, networks computers. Google claims it has unparalleled accuracy, and the learning computers become more accurate over time and more people …show more content…
Semantics. Compositional semantics is the construction of meaning (generally expressed as logic) based on syntax.
4. Pragmatics: meaning in context.

Dept. of CSE Jan-May, 2016 2
Saving Data using speech-to-text and Retrieval using Natural Language Processing

1.2.3 Why is language processing difficult?

Consider trying to build a system that would answer email sent by customers to a retailer selling laptops and accessories via the Internet. This might be expected to handle queries such as the following:

-Has my order number 4291 been shipped yet? -Is FD5 compatible with a 505G?
-What is the speed of the 505G?

Assume the query is to be evaluated against a database containing product and order information, with relations such as the following:

ORDER
Order number Date ordered Date shipped
4290 2/2/02 2/2/02
4291 2/2/02 2/2/02
4292 2/2/02

USER: Has my order number 4291 been shipped yet?

DB QUERY: order (number=4291,date shipped=?)

RESPONSE TO USER: Order number 4291 was shipped on 2/2/02

It might look quite easy to write patterns for these queries, but very similar strings can mean very different things, while very different strings can mean much the same thing. 1 and 2 below look very similar but mean something completely different, while 2 and 3 look very different but mean much the same

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