Paper Info
Title
Learning parse structure of paragraphs and its applications in search.
Abstract
We propose to combine parse forest and discourse structures to form a unified representation for a paragraph of text. The purpose of this representation is to tackle answering complex paragraph-sized questions in a number of products and services-related domains. A candidate set of answers, obtained by a keyword search, is re-ranked by matching the sequence of parse trees of an answer with that of the question. To do that, a graph representation and learning technique for parse structures for paragraphs of text have been developed. Parse thicket (PT) as a set of syntactic parse trees augmented by a number of arcs for inter-sentence word–word relations such as co-reference and taxonomic relations is introduced. These arcs are also derived from other sources, including Speech Act and Rhetoric Structure theories. The operation of generalization of logical formulas is extended towards parse trees and then towards parse thickets to compute similarity between texts.
Year
DOI
Venue
2014
10.1016/j.engappai.2014.02.013
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Parse forest,Parse thicket,Graph learning
Search engine,Expression (mathematics),Computer science,Keyword search,Paragraph,Natural language processing,Artificial intelligence,Plug-in,Parsing,Syntax,Machine learning,Graph (abstract data type)
Journal
Volume
ISSN
Citations 
32
0952-1976
7
PageRank 
References 
Authors
0.49
38
1
Name
Order
Citations
PageRank
Boris Galitsky124837.81