UniOntBot: Semantic Natural Language Generation based API approach for Chatbot Communication

Gunasekara, Lakindu; Vidanage, Kaneeka

doi:10.1109/nitc48475.2019.9114440

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…Additionally, Rasa Core has high Natural Language Generation (NLG) capabilities [16], [17]. Therefore, upon retrieval, the Rasa Core uses its NLG capabilities to prepare a natural language human-like response to the poultry farmer based on the intent and context information returned from the Rasa NLU [17].…”

Section: Methodsmentioning

confidence: 99%

“…The dialogue manager generates raw responses that will be passed to the Natural Language Generator component that refines the text response and construct the understandable text responses in natural human language in machine representation. NLG process converts structured data into text, therefore it generates an appropriate response that a human can understand [16]. Finally, the generated feedback is sent back to the farmer"s mobile application User interface via the API.…”

Section: Methodsmentioning

confidence: 99%

“…The training data includes several intents: greet, goodbye, chicken_feeds, chicken_breeds, eggs_production, chicken_diseases, and chicken_shelter. We use about 200 sample questions and answers with marked entities to train the Rasa NLU [15], [16].…”

Section: Training Of the Rasa Conversational System Modelmentioning

confidence: 99%

See 2 more Smart Citations

Mobile-based Decision Support System for Poultry Farmers: A Case of Tanzania

Shapa¹,

Trojer²,

Machuve³

2021

IJACSA

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Poultry farms in Tanzania are characterized by inadequate management practices which are mainly caused by the lack of adequate systems to guide the small-scale poultry farmers in decision making. It is well-established that information is a key factor in making effective decisions in numerous sectors including poultry farming. Furthermore, various researchers have identified the use of mobile decision support tools to be an effective way of aiding farmers in making informed decisions. In this paper, we present a mobile-based decision support system that will aid rural and small-scale poultry farmers in Tanzania to obtain reliable information that is crucial for making proper decisions in their farming activities. In this context, a mobile-based decision support system was achieved through a mobile application integrated with a chatbot assistant to provide a solution to various poultry farming-related problems and simplify their decision-making process. We used a data-driven approach towards developing an informational chatbot assistant for Android smartphones that is capable of interacting with small-scale poultry farmers through natural conversations by utilizing the RASA framework.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mobile-based Decision Support System for Poultry Farmers: A Case of Tanzania

Shapa¹,

Trojer²,

Machuve³

2021

IJACSA

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“…In 2019, Lakindu Gunasekara et al [38], The paper introduced approaches using semantic technologies such as NLG and explores the pros and cons of such systems.…”

Section: Literature Reviewmentioning

confidence: 99%

State of Art for Semantic Analysis of Natural Language Processing

Maulud

Zeebaree

Jacksi

et al. 2021

QAJ

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Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

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“…Furthermore, the proposed method obtains a higher precision in answering five forms of questions than the current system. Lakindu Gunasekara, et al [102]. Presents methods using semantic technologies, which NLG, and discusses the pros and cons of such structures.…”

Section: Topic Modelingmentioning

confidence: 99%

Review on Natural Language Processing Based on Different Techniques

Maulud

Ameen

Omar

et al. 2021

AJRCoS

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With the exponential growth of the information on the Internet, there is a high demand for making this information readable and processable by machines. For this purpose, there is a need for the Natural Language Processing (NLP) pipeline. Natural language analysis is a tool used by computers to grasp, perceive, and control human language. This paper discusses various techniques addressed by different researchers on NLP and compares their performance. The comparison among the reviewed researches illustrated that good accuracy levels haved been achieved. Adding to that, the researches that depended on the Sentiment Analysis and ontology methods achieved small prediction error. The syntactic analysis or parsing or syntax analysis is the third stage of the NLP as a conclusion to use NLP technology. This step aims to accurately mean or, from the text, you may state a dictionary meaning. Syntax analysis analyzes the meaning of the text in comparison with the formal grammatical rules.

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UniOntBot: Semantic Natural Language Generation based API approach for Chatbot Communication

Cited by 9 publications

References 6 publications

Mobile-based Decision Support System for Poultry Farmers: A Case of Tanzania

Mobile-based Decision Support System for Poultry Farmers: A Case of Tanzania

State of Art for Semantic Analysis of Natural Language Processing

Review on Natural Language Processing Based on Different Techniques

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