scaling semantic parsers with on-the-fly ontology...
TRANSCRIPT
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Scaling Semantic Parsers with On-the-Fly Ontology Matching
Tom Kwiakowski, Eunsol Choi, Yoav Artzi, Luke Zettlemoyer.
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Semantic Parsing
Semantic Parser
Q: How many people live in Seattle?
MR:
620,778
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Open Domain QA
7,000
What are the symptoms of prostate cancer?
What architectural style is the Brooklyn Bridge?
How many people ride the monorail in Seattle daily?
Who managed Liverpool F.C. from 2004 to june 2010?QA
QA
QA
QA
Rafael Benitez
Gothic Revival architecture
{Hematuria, Nocturia, Dysuria, ... }
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Open Domain QA
7,000
What are the symptoms of prostate cancer?
What architectural style is the Brooklyn Bridge?
How many people ride the monorail in Seattle daily?
Who managed Liverpool F.C. from 2004 to june 2010?QA
QA
QA
QA
Rafael Benitez
Gothic Revival architecture
{Hematuria, Nocturia, Dysuria, ... }
‣ 40 Million Entities‣ 2 Billion Facts‣ 20,000 Relations‣ 10,000 Types‣ 100 Domains
is a community authored knowledge base with:
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How many people live in Seattle?
Query is Domain Dependent
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How many people live in Seattle?
Person HomeEunsol SeattleLuke SeattleJane Boston
Query is Domain Dependent
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How many people live in Seattle?
City PopulationSeattle 620778Boston 636479
Person HomeEunsol SeattleLuke SeattleJane Boston
Query is Domain Dependent
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How many people live in Seattle?
City PopulationSeattle 620778Boston 636479
Person HomeEunsol SeattleLuke SeattleJane Boston
• Requires different syntax for different domains➡ Grammars do not generalize well➡ Grammars are hard to learn
Query is Domain Dependent
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City PopulationSeattle 450,000Boston 750,000
How many people live in Seattle?
Person AwardNelson M. Nobel P.P.Mother T. Nobel P.P.Leymah G. Nobel P.P.
How many people have won the Nobel peace prize?
Query is Domain Dependent
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City PopulationSeattle 620778Boston 636479
How many people live in Seattle?
Person AwardNelson M. Nobel P.P.Mother T. Nobel P.P.Leymah G. Nobel P.P.
How many people have won the Nobel peace prize?
Query is Domain Dependent
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How many people live in Seattle?
2 Stage Semantic Parsing
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How many people live in Seattle?
2 Stage Semantic Parsing1. Domain independent, linguistically motivated parse.
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How many people live in Seattle?
2 Stage Semantic Parsing
Person HomeEunsol SeattleLuke SeattleJane Boston
2. Domain specific ontology match.
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City PopulationSeattle 620778Boston 636479
How many people live in Seattle?
2 Stage Semantic Parsing
Person HomeEunsol SeattleLuke SeattleJane Boston
2. Domain specific ontology match.
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City PopulationSeattle 620778Boston 636479
How many people live in Seattle?
2 Stage Semantic Parsing
Person HomeEunsol SeattleLuke SeattleJane Boston
2. Domain specific ontology match.
• All domains use same syntax that generalizes well
• Ontology match can be guided by the structure of the underspecified logical form
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• 2 stage semantic parsing
➡ Domain independent parsing
➡ Domain dependent ontology matching
• Modeling and Inference
• Learning from Question/Answer pairs
• Experiments
Overview
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City PopulationSeattle 620778Boston 636479
How many people live in Seattle?
2 Stage Semantic Parsing
Person HomeEunsol SeattleLuke SeattleJane Boston
1. Domain independent, linguistically motivated parse. 2. Domain specific ontology match.
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Constant Matchesfor .
2 Stage Semantic ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP�f�g�x.eq(x, count( �x.people(x) �x�ev.live(x, ev) �x�f9ev.in(ev, x) seattle
�y.g(y)^ f(y))) ^ f(ev)> >
<B>
S�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
Domain Independent Parse
Domain Independent Parse
Ontology Match
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.how many people live in(seattle, x)
�x.how many people live in(seattle, x)
�x.population(seattle, x)
Structure Match
Ontology Match
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Constant Matchesfor .
2 Stage Semantic ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP�f�g�x.eq(x, count( �x.people(x) �x�ev.live(x, ev) �x�f9ev.in(ev, x) seattle
�y.g(y)^ f(y))) ^ f(ev)> >
<B>
S�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
Domain Independent Parse
Ontology Match
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.how many people live in(seattle, x)
�x.how many people live in(seattle, x)
�x.population(seattle, x)
Structure Match
Ontology Match
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Constant Matchesfor .
2 Stage Semantic ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP�f�g�x.eq(x, count( �x.people(x) �x�ev.live(x, ev) �x�f9ev.in(ev, x) seattle
�y.g(y)^ f(y))) ^ f(ev)> >
<B>
S�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
Domain Independent Parse
Ontology Match
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.how many people live in(seattle, x)
�x.how many people live in(seattle, x)
�x.population(seattle, x)
Structure Match
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CCGLexicon
CCG Parsing
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CCGLexicon
CCG Parsing
Do not have lexicon for every domain
Use Wiktionary for syntactic cues
Parse to underspecified semantics
➡
➡
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49 domain independent lexical items: Word Syntax Underspecified semanticsHow many ` S/(S\NP )/N : �f�g�x.eq(x, count(�y.f(y) ^ g(y)))What ` S/(S\NP )/N : �f�g�x.f(x) ^ g(x)most ` NP/N : �f.max count(�y.f(y))etc.
56 underspecified lexical categories:
proper noun ` NP : C
noun ` N : �x.P (x)
noun ` N/N : �f�x.f(x) ^ P (x)
verb ` S\NP : ��ev.P (x, ev)
verb ` S\NP/NP : �x�y�ev.P (y, x, ev)
preposition ` N\N/NP : �f�x�y.P (y, x) ^ f(y)
preposition ` S\S/NP : �f�x9ev.P (ev, x) ^ f(ev)
etc.
Part-of-Speech Syntax Underspecified semantics
Domain Independent Parsing
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Domain Independent ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP
�f�g�x.eq(x, count( �x.P (x) �x�ev.P (x, ev) �x�f9ev.P (ev, x) ^ f(ev) C
�y.g(y) ^ f(y)))> >
S/(S\NP ) S\S�g�x.eq(x, count(�y.g(y) ^ P (y))) �f9ev.P (ev, C) ^ f(ev)
<BS\NP
�x9ev.P (x, ev) ^ P (ev, C)>
S
�x.eq(x, count(�y.P (y) ^ 9ev.P (y, ev) ^ P (ev, C)))
�x.eq(x, count(�y.9ev.people(y) ^ live(y, ev) ^ in(ev, seattle)))
24
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Domain Independent ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP
�f�g�x.eq(x, count( �x.P (x) �x�ev.P (x, ev) �x�f9ev.P (ev, x) ^ f(ev) C
�y.g(y) ^ f(y)))> >
S/(S\NP ) S\S�g�x.eq(x, count(�y.g(y) ^ P (y))) �f9ev.P (ev, C) ^ f(ev)
<BS\NP
�x9ev.P (x, ev) ^ P (ev, C)>
S
�x.eq(x, count(�y.P (y) ^ 9ev.P (y, ev) ^ P (ev, C)))
�x.eq(x, count(�y.9ev.people(y) ^ live(y, ev) ^ in(ev, seattle)))
25
Use Wiktionary to get part-of-speech set for words
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49 domain independent lexical items: Word Syntax Underspecified semanticsHow many ` S/(S\NP )/N : �f�g�x.eq(x, count(�y.f(y) ^ g(y)))What ` S/(S\NP )/N : �f�g�x.f(x) ^ g(x)most ` NP/N : �f.max count(�y.f(y))etc.
56 underspecified lexical categories:
proper noun ` NP : C
noun ` N : �x.P (x)
noun ` N/N : �f�x.f(x) ^ P (x)
verb ` S\NP : ��ev.P (x, ev)
verb ` S\NP/NP : �x�y�ev.P (y, x, ev)
preposition ` N\N/NP : �f�x�y.P (y, x) ^ f(y)
preposition ` S\S/NP : �f�x9ev.P (ev, x) ^ f(ev)
etc.
Part-of-Speech Syntax Underspecified semantics
Domain Independent Parsing
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Domain Independent ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP
�f�g�x.eq(x, count( �x.P (x) �x�ev.P (x, ev) �x�f9ev.P (ev, x) ^ f(ev) C
�y.g(y) ^ f(y)))> >
S/(S\NP ) S\S�g�x.eq(x, count(�y.g(y) ^ P (y))) �f9ev.P (ev, C) ^ f(ev)
<BS\NP
�x9ev.P (x, ev) ^ P (ev, C)>
S
�x.eq(x, count(�y.P (y) ^ 9ev.P (y, ev) ^ P (ev, C)))
�x.eq(x, count(�y.9ev.people(y) ^ live(y, ev) ^ in(ev, seattle)))
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Domain Independent ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP
�f�g�x.eq(x, count( �x.P (x) �x�ev.P (x, ev) �x�f9ev.P (ev, x) ^ f(ev) C
�y.g(y) ^ f(y)))> >
S/(S\NP ) S\S�g�x.eq(x, count(�y.g(y) ^ P (y))) �f9ev.P (ev, C) ^ f(ev)
<BS\NP
�x9ev.P (x, ev) ^ P (ev, C)>
S
�x.eq(x, count(�y.P (y) ^ 9ev.P (y, ev) ^ P (ev, C)))
�x.eq(x, count(�y.9ev.people(y) ^ live(y, ev) ^ in(ev, seattle)))
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Domain Independent ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP
�f�g�x.eq(x, count( �x.P (x) �x�ev.P (x, ev) �x�f9ev.P (ev, x) ^ f(ev) C
�y.g(y) ^ f(y)))> >
S/(S\NP ) S\S�g�x.eq(x, count(�y.g(y) ^ P (y))) �f9ev.P (ev, C) ^ f(ev)
<BS\NP
�x9ev.P (x, ev) ^ P (ev, C)>
S
�x.eq(x, count(�y.P (y) ^ 9ev.P (y, ev) ^ P (ev, C)))
�x.eq(x, count(�y.9ev.people(y) ^ live(y, ev) ^ in(ev, seattle)))
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Domain Independent ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP
�f�g�x.eq(x, count( �x.P (x) �x�ev.P (x, ev) �x�f9ev.P (ev, x) ^ f(ev) C
�y.g(y) ^ f(y)))> >
S/(S\NP ) S\S�g�x.eq(x, count(�y.g(y) ^ P (y))) �f9ev.P (ev, C) ^ f(ev)
<BS\NP
�x9ev.P (x, ev) ^ P (ev, C)>
S
�x.eq(x, count(�y.P (y) ^ 9ev.P (y, ev) ^ P (ev, C)))
�x.eq(x, count(�y.9ev.people(y) ^ live(y, ev) ^ in(ev, seattle)))
String labels signify source words, not semantic constants.
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Constant Matchesfor .
2 Step Semantic ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP�f�g�x.eq(x, count( �x.people(x) �x�ev.live(x, ev) �x�f9ev.in(ev, x) seattle
�y.g(y)^ f(y))) ^ f(ev)> >
<B>
S�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
Domain Independent Parse
Ontology Match
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.how many people live in(seattle, x)
�x.how many people live in(seattle, x)
�x.population(seattle, x)
Structure Match
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Structural MatchCollapse and expand subexpressions in underspecified logical form with operators that:
1. Collapse simple typed sub-expression
2. Collapse complex typed sub-expression3. Expand predicate
New example
�x.eq(x, count(�y.people(y) ^ 9e.ride(y, ◆z.monorail(z) ^ in(z, seattle), e) ^ daily(e)))
How many people ride the monorail in Seattle daily?
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1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y, ◆z.monorail(z) ^ in(z, seattle), e) ^ daily(e)))
Structural Match
![Page 34: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/34.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y, ◆z.monorail(z) ^ in(z, seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
entity typed subexpression
Structural Match
![Page 35: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/35.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y, ◆z.monorail(z) ^ in(z, seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
integer typed subexpression
�x.eq(x, how many people ride daily the monorail in seattle)
Structural Match
![Page 36: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/36.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y, ◆z.monorail(z) ^ in(z, seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
integer typed subexpression
�x.eq(x, how many people ride daily the monorail in seattle)
Structural Match
![Page 37: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/37.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y, ◆z.monorail(z) ^ in(z, seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
�x.eq(x, how many people ride daily the monorail in seattle)
Structural Match
![Page 38: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/38.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
Structural Match
![Page 39: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/39.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
Structural Match
![Page 40: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/40.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ ride daily(y,monorail in seattle)))
<e,<e,t>> typed subexpression
Structural Match
![Page 41: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/41.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
<i,<e,t>> typed subexpression
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ ride daily(y,monorail in seattle)))
�x.how many people ride daily(the monorail in seattle, x)
Structural Match
![Page 42: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/42.jpg)
1. Find subexpression with type allowed in KB
2. Replace with new underspecified constant
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
�x.eq(x, count(�y.people(y) ^ ride daily(y,monorail in seattle)))
�x.how many people ride daily(the monorail in seattle, x)
Structural Match
![Page 43: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/43.jpg)
1. Find subexpression with type allowed in KB
�x.eq(x, count(�y.people(y) ^ 9e.ride(y,monorail in seattle), e) ^ daily(e)))
�x.how many people ride daily(the monorail in seattle, x)
Structural Match
![Page 44: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/44.jpg)
�x.how many people ride daily(the monorail in seattle, x)
Structural Match
![Page 45: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/45.jpg)
Constant Match
Replace constants with constants from KB
�x.how many people ride daily(the monorail in seattle, x)
Assume constants have English string labels!
![Page 46: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/46.jpg)
Constant Match
Replace constants with constants from KB
�x.how many people ride daily(the monorail in seattle, x)
![Page 47: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/47.jpg)
Constant Match
Replace constants with constants from KB
�x.how many people ride daily(the monorail in seattle, x)
�x.how many people ride daily(seattle monorail, x)
![Page 48: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/48.jpg)
Constant Match
Replace constants with constants from KB
�x.how many people ride daily(the monorail in seattle, x)
�x.how many people ride daily(seattle monorail, x)
�x.transit system/daily riders(seattle monorail, x)
![Page 49: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/49.jpg)
• 2 stage semantic parsing
➡ Domain independent parsing
➡ Domain dependent ontology matching
• Modeling and Inference
• Learning from Question/Answer pairs
• Experiments
Overview
![Page 50: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/50.jpg)
Constant Matchesfor .
2 Stage Semantic ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP�f�g�x.eq(x, count( �x.people(x) �x�ev.live(x, ev) �x�f9ev.in(ev, x) seattle
�y.g(y)^ f(y))) ^ f(ev)> >
<B>
S�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
Domain Independent Parse
Ontology Match
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.how many people live in(seattle, x)
�x.how many people live in(seattle, x)
�x.population(seattle, x)
Structure Match
z}|
{
d
![Page 51: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/51.jpg)
Scoring Derivations
score(d) = �(d)✓
Derivations are scored using a linear model: d
with feature vector that decomposes over:
• Constant match- Lexical features- Knowledge base features
• Structural match• Domain independent parse
�(d)
![Page 52: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/52.jpg)
• Exact string match• Stemmed string match• Synonym match• Wiktionary gloss overlap
When did Prairie Home Companion first air?
�x.radio program.first broadcast(prairie home companion, x)
�x.when first air(prarie home companion, x)
How high is Niagara Falls? �x.high(niagara falls, x)
�x.location.geocode.elevation(niagara falls, x)
Lexical Features
![Page 53: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/53.jpg)
• Exact string match.• Stemmed string match.• Synonym match.• Wiktionary gloss overlap.
When did Prairie Home Companion first air?
�x.radio program.first broadcast(prairie home companion, x)
�x.when first air(prarie home companion, x)
How high is Niagara Falls? �x.high(niagara falls, x)
�x.location.geocode.elevation(niagara falls, x)
Lexical Featureselevation (plural elevations)1. The act of raising from a lower place, condition, or quality to a higher; said of material
things, persons, the mind, the voice, etc.; as, the elevation of grain; elevation to a throne; elevation to sainthood; elevation of mind, thoughts, or character.
2. The condition of being or feeling elevated; heightened; exaltation.high (comparative higher, superlative highest)1. Being elevated in position or status, a state of being above many things. [quotations ▼]
![Page 54: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/54.jpg)
seattle_monorail
7000
monorail
type
daily_riders
yamaha_motorsport
motorbike_rider
valentino_rossi
Test logical structure to see if it can exist in knowledge base.
Knowledge Base Features
![Page 55: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/55.jpg)
seattle_monorail
7000
monorail
type
daily_riders
yamaha_motorsport
motorbike_rider
valentino_rossi
Predicted Logical Form
�x.daily riders(seattle monorail, x)
�x.motorbike rider(seattle monorail, x)
Knowledge Base Features
Test logical structure to see if it can exist in knowledge base.
![Page 56: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/56.jpg)
seattle_monorail
7000
monorail
type
daily_riders
yamaha_motorsport
motorbike_rider
valentino_rossi
Predicted Logical Form
�x�y.daily riders(x, y) ^ monorail(x)
�x�y.motorbike rider(x, y) ^ monorail(x)
Knowledge Base Features
Test logical structure to see if it can exist in knowledge base.
![Page 57: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/57.jpg)
InferenceOntology matching step is:
• Exponential in arity of most complex predicate
• Polynomial in number of logical symbols
• Linear in the size of the knowledge base
➡ Use dynamic programming➡ Prune heavily according to local score➡ Call constant matching operators greedily
![Page 58: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/58.jpg)
2 Stage Semantic ParsingHow many people live in Seattle
S/(S\NP )/N N S\NP S\S/NP NP�f�g�x.eq(x, count( �x.people(x) �x�ev.live(x, ev) �x�f9ev.in(ev, x) seattle
�y.g(y)^ f(y))) ^ f(ev)> >
<B>
S�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
Domain Independent Parse
Ontology Match
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.population(seattle, x)
z}|
{
d
�x.eq(x, count(�y.9ev.people(y)^ live(y, ev)^ in(ev, seattle)))
�x.how many people live in(seattle, x)
![Page 59: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/59.jpg)
• 2 stage semantic parsing
➡ Domain independent parsing
➡ Domain dependent ontology matching
• Modeling and Inference
• Learning from Question/Answer pairs
• Experiments
Overview
![Page 60: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/60.jpg)
Learning{(xi, ai) : i = 1, . . . , n}Q/A pairs
Knowledge Base, Wiktionary, Underspecified Lexicon
Input
Algorithm
i = 1, . . . , nFor :
✓ = ✓ +1
|C|X
c2C
�(c)� 1
|W |X
w2W
�(w)
Max scoring correct parses of
Margin violating incorrect parses of W
C xi
xi
![Page 61: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/61.jpg)
• 2 stage semantic parsing
➡ Domain independent parsing
➡ Domain dependent ontology matching
• Modeling and Inference
• Learning from Question/Answer pairs
• Experiments
Overview
![Page 62: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/62.jpg)
ExperimentsQ/A from databases - evaluate on exact answer match
Freebase917- 642 training sentences, 275 test sentences- 135 million facts, 18 million entities, 2000 relations
GeoQuery- 600 training sentences, 280 test sentence- 14018 facts, 3839 entities, 12 relations- High degree of compositional complexity
Feature initialization:Lexical - Prefer exact and partial string matchKnowledge base - Prefer concepts in knowledge base
![Page 63: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/63.jpg)
Related WorkLearning From Q/A Pairs:
‣ Clarke et.al. 2010‣ Goldwasser et.al. 2011‣ Liang et.al. 2011 (DCS)‣ Berant et.al. 2013
Learning CCG from Labelled Logical Forms:‣ Kwiatkowski et.al. 2011 (FUBL) ‣ Cai & Yates 2013
![Page 64: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/64.jpg)
Related WorkLearning From Q/A Pairs:
‣ Clarke et.al. 2010‣ Goldwasser et.al. 2011‣ Liang et.al. 2011 (DCS)‣ Berant et.al. 2013
Learning CCG from Labelled Logical Forms:‣ Kwiatkowski et.al. 2011 (FUBL) ‣ Cai & Yates 2013
G
G
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ExperimentsFreebase 917
50
54
58
62
66
70
Cai & Yates Berant et.al. Our Approach
![Page 66: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/66.jpg)
ExperimentsGeoQuery
70
75
80
85
90
95
100
FUBL DCS DCS with L+ Our Approach
![Page 67: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/67.jpg)
Example Parses
How many operating systems is Adobe Flash compatible with?
�x.eq(x, count(�y.software compatibility
.operating system(adobe flash, y)))
![Page 68: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/68.jpg)
Example Parses
Who is the CEO of Save-A-Lot?
�x.person(x)^9y.organization(y, savealot)^board member.leader of(x, y) ^ leadership.role(y, ceo)
![Page 69: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/69.jpg)
Example ErrorsHow many children does Jerry Seinfeld have?
�x.eq(x, count(�y.person.children(y, jerry seinfeld)))
�x.eq(x, count(�y.person.children(jerry seinfeld, y)))
Target:
Prediction:
![Page 70: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/70.jpg)
Example ErrorsWhat programming languages were used for AOL instant messenger?
Target:
Prediction:
�x.languages used(aol instant messenger, x)
^programming language(x)
�x.languages used(aol instant messenger, x)
![Page 71: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/71.jpg)
Future Work
Multiple databases:Who’s going to play Batman in the next movie?
When’s the next flight from Seattle to London?What’s the best restaurant near the Grand Hyatt? Parser
Information Extraction:Alan Turing was a British mathematician, logician, cryptanalyst, and computer scientist.
nationality(AT, UK) ^ notable for(AT, mathematian)
^profession(AT, logic)) ^ research(AT, cryptanalysm)
^notable type(AT, compsci)
![Page 72: Scaling Semantic Parsers with On-the-fly Ontology Matchinghomes.cs.washington.edu/~eunsol/papers/emnlp2013slides.pdf · 2018-11-12 · Open Domain QA 7,000 What are the symptoms of](https://reader034.vdocuments.site/reader034/viewer/2022042407/5f219afcbdd4b235933edfa8/html5/thumbnails/72.jpg)
Questions
?