Improving Text Retrieval Efficiency with Pattern Structures on Parse Thickets

S. Kuznetsov; F. V. Strok; D. Ilvovsky; B. Galitsky

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Improving Text Retrieval Efficiency with Pattern Structures on Parse Thickets

P. 6–21.

Kuznetsov S., Strok F. V., Ilvovsky D., Galitsky B.

We develop a graph representation and learning technique for parse structures for paragraphs of text. We introduce Parse Thicket (PT) as a sum of syntactic parse trees augmented by a number of arcs for inter-sentence word-word relations such as co-reference and taxonomic relations. These arcs are also derived from other sources, including Speech Act and Rhetoric Structure theories. The operation of generalizing logical formulas is extended towards parse trees and then towards parse thickets to compute similarity between texts. We provide a detailed illustration of how PTs are built from parse trees, and generalized. The proposed approach is subject to preliminary evaluation in the product search domain of eBay.com, where user queries include product names, features and expressions for user needs, and query keywords occur in different sentences of an answer. We demonstrate that search relevance is improved by PT generalization.

Language: English

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Keywords: graph representation of text learning syntactic parse tree syntactic generalization search relevance

Publication based on the results of:

Mathematical Models, Algorithms, and Software Tools for the Intelligent Analysis of Big Textual and Structural Data (2013)

In book

Proceedings of the Workshop Formal Concept Analysis Meets Information Retrieval

Vol. 977. , M.: CEUR Workshop Proceedings, 2013.

Finding Maximal Common Sub-parse Thickets for Multi-sentence Search

Galitsky B., Ilvovsky D., Kuznetsov S. et al., , in: Graph Structures for Knowledge Representation and Reasoning Third International Workshop, GKR 2013, Beijing, China, August 3, 2013. Revised Selected Papers Editors: Madalina Croitoru, Sebastian Rudolph, Stefan Woltran, Christophe Gonzales. Springer International Publishing. 2014.: Berlin: Springer, 2014. P. 39–57.

We develop a graph representation and learning technique for parse structures for paragraphs of text. We introduce 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. These arcs are also derived from other sources, including Speech Act and ...

Added: June 7, 2014

Parse thicket representations of text paragraphs

Galitsky B., Ilvovsky D., Kuznetsov S. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Бекасово, 29 мая - 2 июня 2013 г.). В 2-х т.Т. 1: Основная программа конференции. Вып. 12 (19).: М.: РГГУ, 2013. P. 239–255.

We develop a graph representation and learning technique for parse structures for sentences and paragraphs of text. We introduce parse thicket as a set of syntactic parse trees augmented by a number of arcs for intersentence word-word relations such as coreference and taxonomies. These arcs are also derived from other sources, including Rhetoric Structure and Speech Act theory. We introduce ...

Added: November 1, 2013

Parse Thicket Representation for Multi-sentence Search

Galitsky B., Kuznetsov S., Usikov D., , in: Conceptual Structures for STEM Research and Education, 20th International Conference on Conceptual StructuresVol. 7735: Conceptual Structures for STEM Research and Education, 20th International Conference on Conceptual Structures.: Berlin, Heidelberg: Springer, 2013. P. 153–172.

We develop a graph representation and learning technique for parse structures for sentences and paragraphs of text. This technique is used to improve relevance answering complex questions where an answer is included in multiple sentences. We introduce Parse Thicket as a sum of syntactic parse trees augmented by a number of arcs for inter-sentence word-word ...

Added: June 2, 2013