International Journal of Technology

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Intelligent Personal Assistant with Knowledge Navigation


Affiliations
1 Army Institute of Technology, Pune, India
     

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An Intelligent Personal Agent (IPA) is an agent that has the purpose of helping the user to gain information through reliable resources with the help of knowledge navigation techniques and saving time to search the best content. The agent is also responsible for responding to the chat-based queries with the help of Conversation Corpus. We will be testing different methods for optimal query generation. To felicitate the ease of usage of the application, the agent will be able to accept the input through Text (Keyboard), Voice (Speech Recognition) and Server (Facebook) and output responses using the same method. Existing chat bots reply by making changes in the input, but we will give responses based on multiple SRT files. The model will learn using the human dialogs dataset and will be able respond human-like. Responses to queries about famous things (places, people, and words) can be provided using web scraping which will enable the bot to have knowledge navigation features. The agent will even learn from its past experiences supporting semi-supervised learning.

Keywords

NLTK, Turing Test, Lemmatization, Levenstein Distance, Conversation Semantics, Semi-Supervised Learning.
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  • Intelligent Personal Assistant with Knowledge Navigation

Abstract Views: 1028  |  PDF Views: 6

Authors

Amit Kumar
Army Institute of Technology, Pune, India
Rahul Dutta
Army Institute of Technology, Pune, India
Harbhajan Rai
Army Institute of Technology, Pune, India
Rushali Patil
Army Institute of Technology, Pune, India

Abstract


An Intelligent Personal Agent (IPA) is an agent that has the purpose of helping the user to gain information through reliable resources with the help of knowledge navigation techniques and saving time to search the best content. The agent is also responsible for responding to the chat-based queries with the help of Conversation Corpus. We will be testing different methods for optimal query generation. To felicitate the ease of usage of the application, the agent will be able to accept the input through Text (Keyboard), Voice (Speech Recognition) and Server (Facebook) and output responses using the same method. Existing chat bots reply by making changes in the input, but we will give responses based on multiple SRT files. The model will learn using the human dialogs dataset and will be able respond human-like. Responses to queries about famous things (places, people, and words) can be provided using web scraping which will enable the bot to have knowledge navigation features. The agent will even learn from its past experiences supporting semi-supervised learning.

Keywords


NLTK, Turing Test, Lemmatization, Levenstein Distance, Conversation Semantics, Semi-Supervised Learning.

References