Semantic parsing
Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning.[1] Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation,[2] question answering,[1][3] ontology induction,[4] automated reasoning,[5] and code generation.[6][7] The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations.[8] Semantic parsing is one of the important tasks in computational linguistics and natural language processing.
Semantic parsing maps text to formal meaning representations. This contrasts with semantic role labeling and other forms of shallow semantic processing, which do not aim to produce complete formal meanings.[9] In computer vision, semantic parsing is a process of segmentation for 3D objects.[10][11]
History & Background[edit]
Early research of semantic parsing included the generation of grammar manually [12] as well as utilizing applied programming logic.[13] In the 2000s, most of the work in this area involved the creation/learning and use of different grammars and lexicons on controlled tasks,[14][15] particularly general grammars such as SCFGs.[16] This improved upon manual grammars primarily because they leveraged the syntactical nature of the sentence, but they still couldn’t cover enough variation and weren’t robust enough to be used in the real world. However, following the development of advanced neural network techniques, especially the Seq2Seq model,[17] and the availability of powerful computational resources, neural semantic parsing started emerging. Not only was it providing competitive results on the existing datasets, but it was robust to noise and did not require a lot of supervision and manual intervention. The current transition of traditional parsing to neural semantic parsing has not been perfect though. Neural semantic parsing, even with its advantages, still fails to solve the problem at a deeper level. Neural models like Seq2Seq treat the parsing problem as a sequential translation problem, and the model learns patterns in a black-box manner, which means we cannot really predict whether the model is truly solving the problem. Intermediate efforts and modifications to the Seq2Seq to incorporate syntax and semantic meaning have been attempted,[18][19] with a marked improvement in results, but there remains a lot of ambiguity to be taken care of.
Within the field of natural language processing (NLP), semantic parsing deals with transforming human language into a format that is easier for machines to understand and comprehend. This method is useful in a number of contexts:
Semantic parsing aims to improve various applications' efficiency and efficacy by bridging the gap between human language and machine processing in each of these domains.
Evaluation[edit]
The performance of Semantic parsers is also measured using standard evaluation metrics as like syntactic parsing. This can be evaluated for the ratio of exact matches (percentage of sentences that were perfectly parsed), and precision, recall, and F1-score calculated based on the correct constituency or dependency assignments in the parse relative to that number in reference and/or hypothesis parses. The latter are also known as the PARSEVAL metrics.[44]