Semantic Parsing Task
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A Semantic Parsing Task is a semantic NLP task that is a parsing task, where the output is a formal representation.
- Context:
- output: a Semantic Model (in a semantic representation language such as First-Order Logic).
- Performance Metric: Verifiability, Unambiguousness, Canonical Form, Inference, Expressiveness.
- It can range from (typically) being a Natural Language Semantic Parsing Task to being a Computer Program Semantic Parsing Task.
- It can range from being a Shallow Semantic Parsing Task (such as extra-propositional semantic parsing) to being a Deep Semantic Parsing Task.
- It can be solved by a Semantic Parsing System (that implements a semantic parsing algorithm).
- ...
- Example(s):
- Counter-Example(s):
- See: Semantic Linguistic Analysis, Semantic Analysis Task, Sentence-level Semantic Analysis Task.
References
2024
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Semantic_parsing Retrieved:2024-1-24.
- Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation, [1] question answering,[2][3] ontology induction, [4] automated reasoning, [5] and code generation. The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations. [6] 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. [7]
In computer vision, semantic parsing is a process of segmentation for 3D objects. [8] [9] Here, we have attached a system architecture for Semantic Parser to understand more briefly. In this picture it represent a basic architecture of semantic parsing, where the whole process has multiple steps like Token Analyzer takes input from input sentence and generate token, Syntactic Analyzer generate parse tree, and with the help of set of rules it generate the semantic meaning of a sentence. [10]
- Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation, [1] question answering,[2][3] ontology induction, [4] automated reasoning, [5] and code generation. The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations. [6] 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
2016
- (Liang, 2016) ⇒ Percy Liang. (2016). “Learning Executable Semantic Parsers for Natural Language Understanding.” In: Communications of the ACM Journal, 59(9). doi:10.1145/2866568
- QUOTE: A long-standing goal of artificial intelligence (AI) is to build systems capable of understanding natural language.
2009
- http://slpl.cse.nsysu.edu.tw/cpchen/courses/slp/p3_semantics.pdf
- Verifiability: With the representation scheme, it must be possible to compare (or match) the meaning of a sentence against the knowledge base.
- Unambiguousness: linguistic input may have several legitimate interpretations. A desired meaning representation should have the ability to tell which are more likely or unlikely
- Canonical form: It is desired that sentences with the same meaning should be assigned the same representation
- Inference: Inference refers to a system’s ability to draw valid conclusions based on the meaning representation ofinput and/or its store of knowledge
- Expressiveness: The expressiveness of a meaning representation language is a measure of the various meanings it candescribe. In principle, there is a very wide range of input and knowledge base. We want a meaning representation method that canaccurately represent any semantic natural language sentences.
- ↑ Andreas, Jacob, Andreas Vlachos, and Stephen Clark. "Semantic parsing as machine translation." Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vol. 2. 2013.
- ↑ Cite error: Invalid
<ref>tag; no text was provided for refs named:0 - ↑ Berant, Jonathan, et al. "Semantic Parsing on Freebase from Question-Answer Pairs." EMNLP. Vol. 2. No. 5. 2013.
- ↑ Poon, Hoifung, and Pedro Domingos. "Unsupervised ontology induction from text." Proceedings of the 48th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010.
- ↑ Kaliszyk, Cezary, Josef Urban, and Jiří Vyskočil. "Automating formalization by statistical and semantic parsing of mathematics." International Conference on Interactive Theorem Proving. Springer, Cham, 2017.
- ↑ Wilks, Y. and Fass, D. (1992) The Preference Semantics Family, In Computers and Mathematics with Applications, Volume 23, Issues 2-5, Pages 205-221.
- ↑ Hoifung Poon, Pedro Domingos Unsupervised Semantic Parsing , Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009
- ↑ Armeni, Iro, et al. "3d semantic parsing of large-scale indoor spaces." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
- ↑ Qi, Charles R., et al. "Pointnet: Deep learning on point sets for 3d classification and segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- ↑ M. F. Mridha1, Molla Rashied Hussein, Md. Musfiqur Rahaman and Jugal Krishna Das "A PROFICIENT AUTONOMOUS BANGLA SEMANTIC PARSER FOR NATURAL LANGUAGE PROCESSING." Proceedings of ARPN Journal of Engineering and Applied Sciences. Vol. 10. 2015.