The once thought fantastical ability for machines to answer any and every human question is becoming possible with Artificial Intelligence (AI). The computer science field, called Question Answering (QA) is an arguably efficient technology enabling systems to fetch contextual answers for user queries using informative resources. We, at Oodles, as an experiential AI development company, presents a comprehensive introduction to the mechanism of QA systems with NLP (Natural Language Processing).
The Rationale Behind QA Systems
Under Natural Language Processing (NLP), Question Answering or QA is a discipline that enables users to retrieve answers from machines for questions posed in natural language. To curate a meaningful response, QA systems are programmed to-
1) Perform context-based reasoning
2) Identify and classify questions based on training data
3) Search databases or knowledge base, and
4) Construct a response from unstructured knowledge.
For this reason, QA systems with NLP are much harder to build and deploy as compared to chatbots.
Typically, chatbot development services employ third-party frameworks such as Amazon Lex or IBM Watson to build rule-based virtual assistants. QA systems, on the other hand, demand prodigious volumes of data and expertise to retrieve answers for dynamic user queries. For instance, chatbots are programmed to address domain-specific queries, such as weather chatbot, food ordering chatbot, insurance chatbot, et al.
Whereas, QA systems work with extensive databases, basically written information in the form of articles, paragraphs, or web to narrow down answers. A general QA system architecture is given below-
Instead of giving a list of possible answers, NLP enables QA systems to provide a crisp short answer to user queries with techniques such as-
a) POS Tagging
b) Tokenization
c) Named Entity Recognition
d) Semantic Parser, and
e) Similarity Distance
Learn more: A System With NLP for Optimizing Customer Interactions