Contents
What is a domain-specific question?
A Domain-Specific Question Answering System Based on Ontology and Question Templates. The system captures a user’s intention by matching his questions to predefined templates, and return answers corresponding to template’s query focus.
What does Domain-Specific mean?
Domain specificity (also known as domain-specific learning) is a concept in cognitive science that suggests that some cognitive functions are solely responsible for individual specific functions and not multiple ones. Different areas of cognition are referred to as domains.
What is a domain specific theory?
From Wikipedia, the free encyclopedia. Domain-specific learning theories of development hold that we have many independent, specialised knowledge structures (domains), rather than one cohesive knowledge structure. Thus, training in one domain may not impact another independent domain.
How does a question answering system ( QA ) work?
A Question Answering (QA) system aims at satisfying users who are looking to answer a specific question in natural language. It works like search engines, but with different result representations: a search engine returns a list of links to answering resources, while a QA system gives a direct answer to a question.
How to build a question answering system in Python?
In this article, we’ll learn two basic methods of implementing Question Answering systems using Python development. A Question Answering (QA) system aims at satisfying users who are looking to answer a specific question in natural language.
Can a closed domain QA system be used?
On the other hand, closed-domain systems deal with questions under a specific domain (for example, medicine or automotive maintenance), and can exploit domain-specific knowledge by using a model that is fitted to a unique-domain database. The cdQA-suite was built to enable anyone who wants to build a closed-domain QA system easily.
Which is an example of an open domain system?
Open-domain systems deal with questions about nearly anything, and can only rely on general ontologies and world knowledge. One example of such a system is DrQA, an ODQA developed by Facebook Research that uses a large base of articles from Wikipedia as its source of knowledge.