Semantic Parsing for Question Answering Raymond J Mooney
![Semantic Parsing for Question Answering Raymond J. Mooney University of Texas at Austin 1 Semantic Parsing for Question Answering Raymond J. Mooney University of Texas at Austin 1](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-1.jpg)
![Semantic Parsing • Semantic Parsing: Transforming natural language (NL) sentences into completely formal logical Semantic Parsing • Semantic Parsing: Transforming natural language (NL) sentences into completely formal logical](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-2.jpg)
![Geoquery: A Database Query Application • Query application for U. S. geography database containing Geoquery: A Database Query Application • Query application for U. S. geography database containing](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-3.jpg)
![Predicate Logic Query Language • Most existing work on computational semantics is based on Predicate Logic Query Language • Most existing work on computational semantics is based on](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-4.jpg)
![Functional Query Language (Fun. QL) • Transform a logical language into a functional, variable-free Functional Query Language (Fun. QL) • Transform a logical language into a functional, variable-free](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-5.jpg)
![Learning Semantic Parsers • Manually programming robust semantic parsers is difficult due to the Learning Semantic Parsers • Manually programming robust semantic parsers is difficult due to the](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-6.jpg)
![Compositional Semantics • Approach to semantic analysis based on building up an MR compositionally Compositional Semantics • Approach to semantic analysis based on building up an MR compositionally](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-7.jpg)
![Composing MRs from Parse Trees What is the capital of Ohio? S answer(capital(loc_2(stateid('ohio')))) NP Composing MRs from Parse Trees What is the capital of Ohio? S answer(capital(loc_2(stateid('ohio')))) NP](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-8.jpg)
![Disambiguation with Compositional Semantics • The composition function that combines the MRs of the Disambiguation with Compositional Semantics • The composition function that combines the MRs of the](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-9.jpg)
![Composing MRs from Parse Trees What is the capital of Ohio? S NP WP Composing MRs from Parse Trees What is the capital of Ohio? S NP WP](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-10.jpg)
![Composing MRs from Parse Trees What is the capital of Ohio? S VP NP Composing MRs from Parse Trees What is the capital of Ohio? S VP NP](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-11.jpg)
![Experimental Corpora • Geo. Query [Zelle & Mooney, 1996] – – 250 queries for Experimental Corpora • Geo. Query [Zelle & Mooney, 1996] – – 250 queries for](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-12.jpg)
![Experimental Methodology • Evaluated using standard 10 -fold cross validation • Correctness – CLang: Experimental Methodology • Evaluated using standard 10 -fold cross validation • Correctness – CLang:](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-13.jpg)
![Precision Learning Curve for Geo. Query 14 Precision Learning Curve for Geo. Query 14](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-14.jpg)
![Recall Learning Curve for Geoquery 15 Recall Learning Curve for Geoquery 15](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-15.jpg)
![Precision Learning Curve for Geo. Query (WASP) 16 Precision Learning Curve for Geo. Query (WASP) 16](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-16.jpg)
![Recall Learning Curve for Geo. Query (WASP) 17 Recall Learning Curve for Geo. Query (WASP) 17](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-17.jpg)
![Conclusions • Semantic parsing maps NL sentences to completely formal computer language. • Semantic Conclusions • Semantic parsing maps NL sentences to completely formal computer language. • Semantic](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-18.jpg)
- Slides: 18
![Semantic Parsing for Question Answering Raymond J Mooney University of Texas at Austin 1 Semantic Parsing for Question Answering Raymond J. Mooney University of Texas at Austin 1](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-1.jpg)
Semantic Parsing for Question Answering Raymond J. Mooney University of Texas at Austin 1
![Semantic Parsing Semantic Parsing Transforming natural language NL sentences into completely formal logical Semantic Parsing • Semantic Parsing: Transforming natural language (NL) sentences into completely formal logical](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-2.jpg)
Semantic Parsing • Semantic Parsing: Transforming natural language (NL) sentences into completely formal logical forms or meaning representations (MRs). • Sample application domains where MRs are directly executable by another computer system to perform some task. – Database/knowledge-graph queries – Robot command language 2
![Geoquery A Database Query Application Query application for U S geography database containing Geoquery: A Database Query Application • Query application for U. S. geography database containing](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-3.jpg)
Geoquery: A Database Query Application • Query application for U. S. geography database containing about 800 facts [Zelle & Mooney, 1996] Which rivers run through the states bordering Texas? Arkansas, Canadian, Cimarron, Gila, Mississippi, Rio Grande … Answer Semantic Parsing answer(traverse(next_to(stateid(‘texas’)))) Query 3
![Predicate Logic Query Language Most existing work on computational semantics is based on Predicate Logic Query Language • Most existing work on computational semantics is based on](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-4.jpg)
Predicate Logic Query Language • Most existing work on computational semantics is based on predicate logic What is the smallest state by area? answer(x 1, smallest(x 2, (state(x 1), area(x 1, x 2)))) x 1 is a logical variable that denotes “the smallest state by area” 4
![Functional Query Language Fun QL Transform a logical language into a functional variablefree Functional Query Language (Fun. QL) • Transform a logical language into a functional, variable-free](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-5.jpg)
Functional Query Language (Fun. QL) • Transform a logical language into a functional, variable-free language (Kate et al. , 2005) What is the smallest state by area? answer(x 1, smallest(x 2, (state(x 1), area(x 1, x 2)))) answer(smallest_one(area_1(state(all)))) 5
![Learning Semantic Parsers Manually programming robust semantic parsers is difficult due to the Learning Semantic Parsers • Manually programming robust semantic parsers is difficult due to the](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-6.jpg)
Learning Semantic Parsers • Manually programming robust semantic parsers is difficult due to the complexity of the task. • Semantic parsers can be learned automatically from sentences paired with their logical form. NL MR Training Exs Natural Language Semantic-Parser Learner Semantic Parser Meaning Rep 6
![Compositional Semantics Approach to semantic analysis based on building up an MR compositionally Compositional Semantics • Approach to semantic analysis based on building up an MR compositionally](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-7.jpg)
Compositional Semantics • Approach to semantic analysis based on building up an MR compositionally based on the syntactic structure of a sentence. • Build MR recursively bottom-up from the parse tree. Build. MR(parse-tree) If parse-tree is a terminal node (word) then return an atomic lexical meaning for the word. Else For each child, subtreei, of parse-tree Create its MR by calling Build. MR(subtreei) Return an MR by properly combining the resulting MRs for its children into an MR for the overall parse-tree.
![Composing MRs from Parse Trees What is the capital of Ohio S answercapitalloc2stateidohio NP Composing MRs from Parse Trees What is the capital of Ohio? S answer(capital(loc_2(stateid('ohio')))) NP](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-8.jpg)
Composing MRs from Parse Trees What is the capital of Ohio? S answer(capital(loc_2(stateid('ohio')))) NP WP What VP capital(loc_2(stateid('ohio'))) answer() NP V capital(loc_2(stateid('ohio'))) VBZ DT N capital() PP loc_2(stateid('ohio')) is the capital IN loc_2() NP stateid('ohio') capital() of loc_2() NNPstateid('ohio') Ohio stateid('ohio') 8
![Disambiguation with Compositional Semantics The composition function that combines the MRs of the Disambiguation with Compositional Semantics • The composition function that combines the MRs of the](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-9.jpg)
Disambiguation with Compositional Semantics • The composition function that combines the MRs of the children of a node, can return if there is no sensible way to compose the children’s meanings. • Could compute all parse trees up-front and then compute semantics for each, eliminating any that ever generate a semantics for any constituent. • More efficient method: – When filling (CKY) chart of syntactic phrases, also compute all possible compositional semantics of each phrase as it is constructed and make an entry for each. – If a given phrase only gives semantics, then remove this phrase from the table, thereby eliminating any parse that includes this meaningless phrase.
![Composing MRs from Parse Trees What is the capital of Ohio S NP WP Composing MRs from Parse Trees What is the capital of Ohio? S NP WP](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-10.jpg)
Composing MRs from Parse Trees What is the capital of Ohio? S NP WP What VP NP V VBZ is DT PP N the capital IN loc_2() NP riverid('ohio') of loc_2() NNPriverid('ohio') Ohio riverid('ohio') 10
![Composing MRs from Parse Trees What is the capital of Ohio S VP NP Composing MRs from Parse Trees What is the capital of Ohio? S VP NP](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-11.jpg)
Composing MRs from Parse Trees What is the capital of Ohio? S VP NP WP What NP capital() V PPloc_2(stateid('ohio')) VBZ DT N capital() IN loc_2() NP stateid('ohio') NNP of is the capital() loc_2() Ohio stateid('ohio')
![Experimental Corpora Geo Query Zelle Mooney 1996 250 queries for Experimental Corpora • Geo. Query [Zelle & Mooney, 1996] – – 250 queries for](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-12.jpg)
Experimental Corpora • Geo. Query [Zelle & Mooney, 1996] – – 250 queries for the given U. S. geography database 6. 87 words on average in NL sentences 5. 32 tokens on average in formal expressions Also translated into Spanish, Turkish, & Japanese. 12
![Experimental Methodology Evaluated using standard 10 fold cross validation Correctness CLang Experimental Methodology • Evaluated using standard 10 -fold cross validation • Correctness – CLang:](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-13.jpg)
Experimental Methodology • Evaluated using standard 10 -fold cross validation • Correctness – CLang: output exactly matches the correct representation – Geoquery: the resulting query retrieves the same answer as the correct representation • Metrics 13
![Precision Learning Curve for Geo Query 14 Precision Learning Curve for Geo. Query 14](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-14.jpg)
Precision Learning Curve for Geo. Query 14
![Recall Learning Curve for Geoquery 15 Recall Learning Curve for Geoquery 15](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-15.jpg)
Recall Learning Curve for Geoquery 15
![Precision Learning Curve for Geo Query WASP 16 Precision Learning Curve for Geo. Query (WASP) 16](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-16.jpg)
Precision Learning Curve for Geo. Query (WASP) 16
![Recall Learning Curve for Geo Query WASP 17 Recall Learning Curve for Geo. Query (WASP) 17](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-17.jpg)
Recall Learning Curve for Geo. Query (WASP) 17
![Conclusions Semantic parsing maps NL sentences to completely formal computer language Semantic Conclusions • Semantic parsing maps NL sentences to completely formal computer language. • Semantic](https://slidetodoc.com/presentation_image_h/26a6a337e86241166afaaf5e236ad1ea/image-18.jpg)
Conclusions • Semantic parsing maps NL sentences to completely formal computer language. • Semantic parsers can be effectively learned from supervised corpora consisting of only sentences paired with their formal representations. • Can reduce supervision demands by training on questions and answers rather than formal representations. – Results on Free. Base queries and queries to corpora of web tables. • Full question answering is finally taking off as an application due to: – Availability of large scale, open databases such as Free. Base, DBPedia, Google Knowledge Graph, Bing Satori – Availability of speech interfaces that allow more natural entry of full NL questions.
Semantic parsing
Semantic parsing
Semantic parsing
Answering
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