Designing effective chatbot system using gru with beam search (Record no. 19040)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230327091553.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230327b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20268 |
| Author | Thorat, Sandeep A. |
| 245 ## - TITLE STATEMENT | |
| Title | Designing effective chatbot system using gru with beam search |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.13(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 2750-2755p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <br/>Artificial Intelligence (AI) based Chatbot is a moderately new<br/>technology in the world. AI and Natural Language Processing (NLP)<br/>empowers a Chatbot to converse like a human being. Chatbots have<br/>become popular recently as they diminish human efforts by automating<br/>various tasks. AI-based Chatbot learns from the previous discussion<br/>and generates an appropriate response or action for the input given by<br/>the user. In the proposed research work we designed AI-based Chatbot<br/>system using the Sequence to Sequence (Seq2Seq) model. This system<br/>uses a Gated Recurrent Unit (GRU) for encoder and decoder. In the<br/>proposed model the GRU encoder accepts a query from the user. The<br/>GRU encoder uses an attention mechanism to consider only relevant<br/>information and convert it into the context vector form. A context vector<br/>is another input to the GRU decoder. The GRU decoder generates a<br/>response using the Beam search algorithm. The research work uses<br/>Cornell Movie Dialogue Corpus to train the proposed interactive<br/>Chatbot system. It is observed that the proposed model with the<br/>combination of GRU and Beam search gives better accuracy with the<br/>minimum loss for testing data. These experimental results are better<br/>than existing approaches that use LSTM Seq2Seq models to train<br/>Chatbot systems. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20269 |
| Co-Author | Jadhav, Vishakha D. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_2_2750_2755.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 27/03/2023 | 2023-0511 | 27/03/2023 | 27/03/2023 | Articles Abstract Database |