scholarly journals Smart Hotel Using Intelligent Chatbot : A Review

2019 ◽  
pp. 823-829
Author(s):  
Shubham Parmar ◽  
Megha Meshram ◽  
Parth Parmar ◽  
Meet Patel ◽  
Payal Desai
Keyword(s):  
Natural Language ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Language Understanding ◽  
Language Generation

Intelligent Chatbot, Natural Language Understanding, Natural Language Generation, NLP, WIT, API, LUIS

Guru

2012 ◽  
pp. 156-171 ◽  
Author(s):  
Andrew M. Olney ◽  
Natalie K. Person ◽  
Arthur C. Graesser
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Language Understanding ◽  
Language Generation ◽  
Processing Techniques

The authors discuss Guru, a conversational expert ITS. Guru is designed to mimic expert human tutors using advanced applied natural language processing techniques including natural language understanding, knowledge representation, and natural language generation.

scholarly journals Designing and Implementing Conversational Intelligent Chat-bot Using Natural Language Processing

2021 ◽  
pp. 262-266
Author(s):  
Asoke Nath ◽  
Rupamita Sarkar ◽  
Swastik Mitra ◽  
Rohitaswa Pradhan
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Natural Language Understanding ◽  
Natural Language Generation ◽  
Representation Learning ◽  
Language Understanding ◽  
Language Generation ◽  
User Input

In the early days of Artificial Intelligence, it was observed that tasks which humans consider ‘natural’ and ‘commonplace’, such as Natural Language Understanding, Natural Language Generation and Vision were the most difficult task to carry over to computers. Nevertheless, attempts to crack the proverbial NLP nut were made, initially with methods that fall under ‘Symbolic NLP’. One of the products of this era was ELIZA. At present the most promising forays into the world of NLP are provided by ‘Neural NLP’, which uses Representation Learning and Deep Neural networks to model, understand and generate natural language. In the present paper the authors tried to develop a Conversational Intelligent Chatbot, a program that can chat with a user about any conceivable topic, without having domain-specific knowledge programmed into it. This is a challenging task, as it involves both ‘Natural Language Understanding’ (the task of converting natural language user input into representations that a machine can understand) and subsequently ‘Natural Language Generation’ (the task of generating an appropriate response to the user input in natural language). Several approaches exist for building conversational chatbots. In the present paper, two models have been used and their performance has been compared and contrasted. The first model is purely generative and uses a Transformer-based architecture. The second model is retrieval-based, and uses Deep Neural Networks.

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
Author(s):  
A. Burgun ◽  
L. P. Seka ◽  
D. Delamarre ◽  
P. Le Beux
Keyword(s):  
Natural Language ◽  
Patient Discharge ◽  
Natural Language Understanding ◽  
Assessment Procedure ◽  
Coding System ◽  
Language Understanding ◽  
Processing Scheme ◽  
Automated Coding ◽  
Conceptual Graph ◽  
Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

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