Problem Solving of Chatbots

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Chabot’s is a developing system which now-days take control in many areas by using its AI (Artificial intelligent) system. A lot of problem is solved and it also being helping kind of system for older people or professional people whose working high research areas. But the system has a certain problem in NLP domain areas where the system gets confused or giving wrong information to the particular user when system leads to incorrect capture of voice recognition or else the command which given which tends to various states(i.e., like some language has an same pronunciations)which have of different meanings.

In this paper we are solving chatbots problem by using a question answering approaches with the help of Corpus data sets, since many paper that discussed about many approaches to solve the problem faced by chatbots. Lexical answers type prediction where some question have some type of hint where the answers we are looking for. It is a most confused to the system when the question which is having more than 5 types answers and that causes to failure. In order to overcome those types’ problems in the chatbots we include corpus dataset to get the correct conversation between the system and user.

And also breaking down the given sentence into two parts first half of finding the lexical in the question and matching the answer (to that type of question). To evaluate the system we have used the label based evaluation in the form of single label evaluation in order to make sure the system performance of each question answers and show the average performance value all related lexical answer type.

A chatbot is a bit of innovation that permits a PC program to impart with people just like conversing through text messaging using a natural language, say English, to accomplish specific tasks. A chatbot is also known as an artificial conversational entity, chat robot, talk bot, chatterbot or chatterbox. It was a simple program designed to impersonate a psychotherapist and give out predefined responses to user queries.

However, the code base was exhaustive enough to take into account several possible queries and the chatbot was capable of passing the Turing Test, a test designed to check out whether a computer program could pass as an actual human being or not. In this paper we are solving chatbot problem by using question answering approaches with help of corpus data sets, since many papers that discussed about many approaches to solve the problem faced by chatbots. Chatbots today, have become a lot more advanced since then, able to answer substantially complex queries and have expanded capabilities such as voice interaction and machine learning.

A content to-discourse innovation is just one that changes over verbal discourse to content on an advanced page. That’s its full function, but it’s not one that is simple to design. The chatbot, then again, will take discourse in whichever structure it’s made for, get it, and give reactions that look to breeze through the turing assessment – the trial of whether an innovation can trick a human into believing that the individual is talking with someone else.

Cite this paper

Problem Solving of Chatbots. (2021, Jan 25). Retrieved from https://samploon.com/problem-solving-of-chatbots/

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