Semantic Parsing is a sub-task of Natural Language Processing (NLP) that deals with mapping natural language to a representation of its meaning in a form that can be understood by computer applications. Semantic Parsing is defined as the task of converting a sentence (or other input) into an equivalent representation of its meaning expressed in a formal language such as a programming language, an ontology, or a Knowledge Representation Language.
The purpose of Semantic Parsing is to have a computer interpret a sentence or other input and carry out the resulting action, performing tasks like question answering, automated reasoning, and dialogue systems. This is typically done by transforming a natural language input into a formal representation or language, which accurately predicts the logical form underlying the input. To accomplish this, parsing techniques and algorithms are used to assign a meaning to the input expressed in a logical language or ontology.
In addition to parsing natural language, semantic parsing is also used in tasks such as information extraction, semantic search, and knowledge base construction. In these tasks, the formal representation produced by a semantic parser may be transformed into an answer, summary, or ontology. These results can then be used to better inform subsequent decisions or take automated actions.
Semantic Parsing is a rapidly advancing field with applications in many areas such as artificial intelligence, education, healthcare, and engineering. For example, it has recently been used in automated tasks such as virtual assistant applications, chatbots, text-to-speech systems, and conversational agents, where the systems must not only understand the meaning of user input but also be able to act upon it to provide a useful answer.
Semantic parsing is an important area for research as it aims to bridge the gap between natural language and computer understandability. As such, it is an integral part of creating technologies that can accurately interpret user input in order to provide useful and reliable answers.
