1. Learning to Parse Database Queries Using Inductive Logic Programming
施林锋
南京大学计算机科学与技术系

2. Outline Introduction Learning to Parse DB Queries Experimental
Overview of CHILL
Parsing DB Queries
Experimental
Future work & Conclusions
References & Related articles

3. Introduction
Empirical or corpus-based methods for constructing natural language systems replace hand-generated
Statistical and probabilistic to constructing parsers
Stochastic grammars(Black,Lafferty, …)
Transition networks(Miller et al.)
Acid test for empirical methods
Construction of better natural language systems
The author is aim to use CHILL to engineer a natural language front- end for a database-query task.

4. Overview of CHILL
CHILL: Constructive Heuristics Induction for Language Learning
CHILL is a general approach to the problem of inducing natural language parsers.
Chill use inductive logic programming to learn a deterministic shift- reduce parser written in Prolog.
Input:
A set of training instances <sentence, desired parses>
Output:
Shift-reduce parser maps sentence to parses
positive examples + negative examples + background knowledge ⇒ hypothesis.

5. Overview of CHILL

6. Parsing DB Queries Example Query language
What is the capital of the state with the largest population?
answer(C, (capital(S,C),largest(P,(state(S),population(S,P))))).
What are the major cities in Kansas?
answer(C, (major(C), city(C), loc(C, S), equal(S, stateid(Kansas))))
Query language
Logical form
More straightforward from natural language utterances than SQL

7. Parsing DB Queries Database United States geography database system
An existing natural language interface called Geobase
Geobase contains 800 Prolog facts about state, capital city, population, area, major rivers, major cities, highest and lowest points

8. Parsing DB Queries
Query language – Geoquery
Basic Objects

9. Parsing DB Queries Query language – Geoquery Basic relations (right)
Meta-predicate (left)

10. Expreimental 250 sentences with its parses Question pattern:
which states | where is | what be/states/rivers (totally 203)
how many/long/large/high (totally 41)
give me…
name the rivers in arkansas (totally 6)
Mainly ask states,rivers,city,population attach with superlative

11. Expreimental Random splits
225 training example, 25 test
10 fold cross validation
Sentence use CHILL to produce query, then executed the query
Evaluation
Same answer score correct, otherwise false

12. Expreimental Result CHILL outperforms the existing
system when trained on 175
or more examples
Two different failure
Wrong parses
Wrong answer
In the Best trial, CHILL ‘s induced parser comprising 1100 lines of Prolog code achieved 84% accuracy in answering novel queries
175 training examples, CHILL produced 3.2% spurious parses, dropping to 2.3% at 200 examples.

13. Conclusions & Future work
CHILL parsers outperform an existing system
Empirical approach is important to NLP application
Future work
Much larger corpora and other domain
Extent to which performance can be improved by corpus “manufacturing”

14. References & Related articles
Zelle J M, Mooney R J. Learning semantic grammars with constructive inductive logic programming[C]//AAAI. 1993:
Zelle J M, Mooney R J. Inducing deterministic Prolog parsers from treebanks: A machine learning approach[C]//AAAI. 1994:
Zettlemoyer L S, Collins M. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars[J]. arXiv preprint arXiv: , 2012.
P：96.25，R：79.29

15. Q & A