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Arabic Dataset for Farmer's Intent Identification toward Developing a Chatbot


Affiliations
1 Climate Change Information Center & Renewable Energy & Expert Systems, Agricultural Research Center, Giza, Egypt
 

A chatbot is an application of artificial intelligence in natural language processing and speech recognition. It is a computer program that imitates humans in making conversations with other people. Chatbots that specialize in a single topic, such as agriculture, are known as domain-specific chatbots. In this paper, we present a dataset for farmer intents. Intent identification is the first step in building a chatbot. The dataset includes five intents (pest or disease identification, irrigation, fertilization, weed identification, and plantation date). The length of the dataset is 720 records. We applied a Multi-Layers Perceptron (MLP) for intent classification. We tried different numbers of neurons per hidden layer and compared between increasing the number of neurons with the fixed number of epochs. The result shows that as the number of neurons in the hidden layers increases, the introduced MLP achieves high accuracy in a small number of epochs. MLP achieves 97% accuracy on the introduced dataset when the number of neurons in each hidden layer is 256 and the number of epochs is 10.

Keywords

Chatbot, Intent Classification, Dataset, Deep Neural Networks.
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  • Arabic Dataset for Farmer's Intent Identification toward Developing a Chatbot

Abstract Views: 287  |  PDF Views: 120

Authors

Abdelrahman Elsayed
Climate Change Information Center & Renewable Energy & Expert Systems, Agricultural Research Center, Giza, Egypt
Susan F. Ellakwa
Climate Change Information Center & Renewable Energy & Expert Systems, Agricultural Research Center, Giza, Egypt

Abstract


A chatbot is an application of artificial intelligence in natural language processing and speech recognition. It is a computer program that imitates humans in making conversations with other people. Chatbots that specialize in a single topic, such as agriculture, are known as domain-specific chatbots. In this paper, we present a dataset for farmer intents. Intent identification is the first step in building a chatbot. The dataset includes five intents (pest or disease identification, irrigation, fertilization, weed identification, and plantation date). The length of the dataset is 720 records. We applied a Multi-Layers Perceptron (MLP) for intent classification. We tried different numbers of neurons per hidden layer and compared between increasing the number of neurons with the fixed number of epochs. The result shows that as the number of neurons in the hidden layers increases, the introduced MLP achieves high accuracy in a small number of epochs. MLP achieves 97% accuracy on the introduced dataset when the number of neurons in each hidden layer is 256 and the number of epochs is 10.

Keywords


Chatbot, Intent Classification, Dataset, Deep Neural Networks.

References