International Journal of Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Intelligent Personal Assistant with Knowledge Navigation


Affiliations
1 Army Institute of Technology, Pune, India
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Alston, W. P. (2000). Illocutionary acts and sentence meaning. Ithaca, NY: Cornell University Press.

  • Austin, J. (1962). How to do things with words. Oxford University Press.

  • Bobrow, D. G., Kaplan, R. M., Kay, M., Norman, D. A., Thompson, H., and Winograd, T. (1977). Gus: A frame-driven dialog system. Artificial Intelligence, 8, 155–173.

  • Chakrabarti, C. (2014). Artificial conversations for chatter bots using knowledge representation, learning, and pragmatics (Ph.D. thesis). University of New Mexico. Albuquerque, NM.

  • Chakrabarti,C. and George F. Luger(2015) Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics. Elsevier, University of New Mexico, Albuquerque, USA

  • Changeux, J. P. (1998). Conversations on mind, matter, and mathematics. Princeton University Press.

  • Clarke, D. (1983). Language and action. In A structural model of behavior. Pergamon Press.

  • Craig, R., and Tracy, K. (1983). Conversational coherence (Vol. 2). Sage Publications.

  • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41, 391–407.

  • Filisko, E., and Seneff, S. (2003). A context resolution server for the galaxy conversational systems, in: Proc. eurospeech.

  • Fisel, M. (2007). Machine learning techniques in dialogue act recognition. In Estonian papers in applied linguistics.

  • Garfinkel, H. (1967). Studies in ethnomethodology. Englewood Cliffs, NJ: Prentice Hall.

  • Ginzburg, J. (2008). Semantics for conversation. King’s College, London: CSLI Publications.

  • Horvitz, E., and Paek, T. (2000). A computational architecture for conversation. Technical Report. Microsoft Research.

  • Kaelbling, L., Littman, M., and Cassandra, A. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence Journal, 99–134.

  • Li, Y. (2004). A method for measuring sentence similarity and its application to conversational agents. In Florida artificial research society conference.

  • Mauldin, M. (1994). Chatterbots, tinymuds, and the turing test: Entering the loebner prize competition. In Proceedings of the 11th national conference on artificial intelligence. Seattle, Washington: AAAI Press.

  • Metallinou, A., Bohus, D., and Williams, J. D. (2013). Discriminative state tracking for spoken dialog systems. In Proceedings of annual meeting of the association for computational linguistics (ACL), Sofia, Bulgaria.

  • Mey, J. L. (2001). Pragmatics: An introduction (2nd ed.). Oxford: Blackwell.

  • Moldovan, C., Rus, V., and Graesser, A. (2011). Automated speech act classification for online chat, In The 22nd midwest artificial intelligence and cognitive science conference.

  • O’Shea, K., Bandar, Z., and Crockett, K. (2009b). Towards a new generation of conversational agents using sentence similarity. Advances in Electrical Engineering and Computational Science, Lecture Notes in Electrical Engineering, 39, 505–514.

  • O’Shea, K., Bandar, Z., Crockett, K., and Mclean, D. (2004). A comparative study of two short text semantic similarity measures. Lecture Notes on Artificial Intelligence, 4953, 172.

  • O’Shea, K., Bandar, Z., and Crockett, K. (2008). A novel approach for constructing conversational agents using sentence similarity measures. In World congress on engineering, international conference on data mining and knowledge engineering (pp. 321–326).

  • Paek, T., and Horvitz, E. (2000). Conversation as action under uncertainty. In Proceedings of the 16th conference on uncertainty in artificial intelligence (pp. 455–464).

  • Pang, B., and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1–135.

  • Pask, G. (1976). Conversation theory. Elsevier.

  • Polifroni, J., and Seneff, S. (2000). Galaxy-II as an architecture for spoken dialogue evaluation. In LREC.

  • Pomerantz, A. (1984). Agreeing and disagreeing with assessments: Some features of preferred/dispreferred turn shapes. Structures of social action: Studies in conversation analysis.

  • Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14, 130–137.

  • Rieser, V., and Lemon, O. (2013). Reinforcement learning for adaptive dialogue systems: A data-driven methodology for dialogue management and natural language generation. Springer.

  • Sammut, C. (2001). Managing context in a conversational agent. Electronic Transactions on Artificial Intelligence, 3, 1–7.

  • Saygin, A. P., and Ciceklib, I. (2002). Pragmatics in human–computer conversation. Journal of Pragmatics, 34, 227–258.

  • Sidnell, J. (2010). Conversation analysis: An introduction. Wiley-Blackwell.

  • Sidnell, J., and Stivers, T. (2012). Handbook of conversation analysis. Wiley-Blackwell.


Abstract Views: 817

PDF Views: 6




  • Intelligent Personal Assistant with Knowledge Navigation

Abstract Views: 817  |  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