Natural Language Processing in Theoretical Application

Summary of Natural Language Processing in Theoretical Applications In this article we will explain the creation and implementation of devices that use the concept of natural language processing and apply them to our daily activities. This device is a carry-in unit that can accommodate several devices people use everyday, such as cars, kitchen utensils, and computers. The device is portable, compact, durable and adaptable. This device can be applied not only to any device, but also to the ability of the device to interact with it, it must be incorporated into the object the device interacts with.

Abstract: Natural language processing involves a close relationship between behavior (language generation) and perception (language understanding). We believe that similar theoretical principles generally apply to language processing and behavior / perception as well. Language generation is not only driven by speaker's intention, language understanding is not only input driven, but production and perception use a common representative. We will associate the latest knowledge of our language production laboratory with the feature binding principle of event coding theory (TEC).

RNN has many uses. A wonderful application uses natural language processing (NLP). Many people are showing RNNs on the Internet, and they have created amazing models that can represent language models. These language models can enter massive Shakespeare poetry After training these models, they can create their own Shakespeare poetry that is difficult to distinguish from the original! Another great application of RNN is machine translation. This method is very interesting as it involves training two RNNs at the same time. In these networks, the input is a pair of sentences in different languages. For example, you can provide English texts paired with French translations to the network. If you receive sufficient training, you can provide English texts to the Web and translate it into French. This model is called sequence 2 sequence model or encoder decoder model.