Paper Info
Title
Incorporating Linguistics Constraints into Inductive Logic Programming.
Abstract
We report work on effectively incorporating linguistic knowledge into grammar induction. We use a highly interactive bottom-up inductive logic programming (ILP) algorithm to learn 'missing' grammar rules from an incomplete grammar. Using linguistic constraints on, for example, head features and gap threading, reduces the search space to such an extent that, in the small-scale experiments reported here, we can generate and store all candidate grammar rules together with information about their coverage and linguistic properties. This allows an appealingly simple and controlled method for generating linguistically plausible grammar rules. Starting from a base of highly specific rules, we apply least general generalisation and inverse resolution to generate more general rules. Induced rules are ordered, for example by coverage, for easy inspection by the user and at any point, the user can commit to a hypothesised rule and add it to the grammar. Related work in ILP and computational linguistics is discussed.
Year
DOI
Venue
2000
10.3115/1117601.1117647
CoNLL/LLL
Keywords
Field
DocType
candidate grammar rule,linguistic property,linguistics constraint,linguistically plausible grammar rule,incomplete grammar,linguistic knowledge,grammar induction,linguistic constraint,inductive logic programming,grammar rule,general rule,general generalisation,bottom up,search space
Attribute grammar,Computer science,Operator-precedence grammar,Artificial intelligence,Natural language processing,Regular grammar,Mildly context-sensitive grammar formalism,ID/LP grammar,Relational grammar,Emergent grammar,Adaptive grammar,Linguistics,Machine learning
Conference
Citations 
PageRank 
References 
12
0.85
13
Authors
2
Name
Order
Citations
PageRank
James Cussens150350.29
Stephen Pulman245038.31