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Cognitive canonicalization of natural language queries using semantic strata

Cognitive canonicalization of natural language queries using semantic strata Cognitive Canonicalization of Natural Language Queries Using Semantic Strata SUMAN DEB ROY and WENJUN ZENG, University of Missouri Natural language search relies strongly on perceiving semantics in a query sentence. Semantics is captured by the relationship among the query words, represented as a network (graph). Such a network of words can be fed into larger ontologies, like DBpedia or Google Knowledge Graph, where they appear as subgraphs-- fashioning the name subnetworks (subnets). Thus, subnet is a canonical form for interfacing a natural language query to a graph database and is an integral step for graph-based searching. In this article, we present a novel standalone NLP technique that leverages the cognitive psychology notion of semantic strata for semantic subnetwork extraction from natural language queries. The cognitive model describes some of the fundamental structures employed by the human cognition to construct semantic information in the brain, called semantic strata. We propose a computational model based on conditional random fields to capture the cognitive abstraction provided by semantic strata, facilitating cognitive canonicalization of the query. Our results, conducted on approximately 5000 queries, suggest that the cognitive canonicals based on semantic strata are capable of significantly improving parsing and role labeling performance http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Cognitive canonicalization of natural language queries using semantic strata

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/2539053
Publisher site
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Abstract

Cognitive Canonicalization of Natural Language Queries Using Semantic Strata SUMAN DEB ROY and WENJUN ZENG, University of Missouri Natural language search relies strongly on perceiving semantics in a query sentence. Semantics is captured by the relationship among the query words, represented as a network (graph). Such a network of words can be fed into larger ontologies, like DBpedia or Google Knowledge Graph, where they appear as subgraphs-- fashioning the name subnetworks (subnets). Thus, subnet is a canonical form for interfacing a natural language query to a graph database and is an integral step for graph-based searching. In this article, we present a novel standalone NLP technique that leverages the cognitive psychology notion of semantic strata for semantic subnetwork extraction from natural language queries. The cognitive model describes some of the fundamental structures employed by the human cognition to construct semantic information in the brain, called semantic strata. We propose a computational model based on conditional random fields to capture the cognitive abstraction provided by semantic strata, facilitating cognitive canonicalization of the query. Our results, conducted on approximately 5000 queries, suggest that the cognitive canonicals based on semantic strata are capable of significantly improving parsing and role labeling performance

Journal

ACM Transactions on Speech and Language Processing (TSLP) – Association for Computing Machinery

Published: Dec 1, 2013

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