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Karpagam, K.
- An Intelligent Conversation Agent for Health Care Domain
Abstract Views :154 |
PDF Views:0
Authors
K. Karpagam
1,
A. Saradha
2
Affiliations
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 4, No 3 (2014), Pagination: 772-776Abstract
Human Computer Interaction is one of the pervasive application areas of computer science to develop with multimodal interaction for information sharings. The conversation agent acts as the major core area for developing interfaces between a system and user with applied AI for proper responses. In this paper, the interactive system plays a vital role in improving knowledge in the domain of health through the intelligent interface between machine and human with text and speech. The primary aim is to enrich the knowledge and help the user in the domain of health using conversation agent to offer immediate response with human companion feel.Keywords
Artificial Intelligence, Question Answering, Conversational Agent, HCI, Pattern Matching, Speech Synthesis.
- A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System
Abstract Views :405 |
PDF Views:4
Authors
K. Karpagam
1,
A. Saradha
2
Affiliations
1 Department of Master of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
1 Department of Master of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1447-1451Abstract
Today, the information is growing enormously and it is difficult and tedious task to retrieve the necessary information from that pool. The main area for retrieving relevant answers is called intelligent information retrieval. To achieve this, question and answering system is used. This question and answering plays a major role in user query processing, information retrieval and extracting related information from the information pool. Recently, number of optimization algorithms is introduced to obtain the accurate and better results. Genetic Algorithm and Cuckoo Search are nature inspired meta-heuristic optimization algorithms. In this paper, combination of Genetic Algorithm with Cuckoo Search is applied to the question and answering system. The proposed algorithm is tested with the Amazon review, Trip Advisor and 20 news group data sets. The results are compared with Genetic Algorithm and Cuckoo Search algorithms.Keywords
Document Clustering, Cuckoo Search, Genetic Algorithm, Information Retrieval, Question and Answering.References
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- Deep Learning Approaches for Answer Selection in Question Answering System for Conversation Agents
Abstract Views :152 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 2 (2020), Pagination: 2040-2044Abstract
The conversation agent acts as core interfaces between a system and user in answering users queries with proper responses. Question answering system acquires an important role in the information retrieval field. The deep learning approach enhances the accuracy in answering complex questions. As outcome, the user is receiving the precise answer instead of large document collections. The aim of this paper is to develop a model with deep learning approach for improving answer selection process which supports more relevant answer displaying by conversation agents. To achieve this, word2vector used for word representation and biLSTM attentive model is used for training, testing and disclosure play precise answer. Question type is identified using POS-tagger based Question Pattern analysis (T-QPA) model. The knowledgebase is created from the bench mark datasets bAbI Facebook (simple QA tasks), TREC QA, Yahoo! Answer, Insurance QA dataset. The proposed framework is built by embedding of questions and answers based on bidirectional long short-term memory (biLSTM) attentive models. The similarity between questions and answers has been measured by semantic and cosine similarity. The proposed model reduces the search gap in extracting among user queries and answer sentences in the education domain. The system results are evaluated with the standard metrics MAP, Top 1 accuracy, F1- Score for the answer selection.Keywords
Deep Learning, Question Answering System, Attentive Model, Conversation Agents, Cosine Similarity.References
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- K. Karpagam and A. Saradha, “A Framework For Intelligent Question Answering System using Semantic Context Specific Document Clustering and Wordnet”, Sadhana-Academy Proceedings in Engineering Sciences, Vol. 44, No. 3, pp. 1-10, 2019.
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