This document provides an abstract for a paper that proposes a new problem called Approximate Membership Localization (AML) for dictionary-based entity recognition. AML aims to locate non-overlapping substrings in a document that approximate true matches, without generating redundant overlapped substrings like Approximate Membership Extraction (AME) does. The paper proposes an algorithm called P-Prune that prunes redundant matched substrings to perform AML more efficiently. Experimental results on real-world datasets demonstrate P-Prune's efficiency over baselines and show that AML outperforms AME, including in a proposed web-based join framework application.

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AML EFFICIENT APPROXIMATE MEMBERSHIP LOCALIZATION WITHIN
A WEB-BASED JOIN FRAMEWORK
ABSTRACT:
In this paper, we propose a new type of Dictionary-based Entity Recognition Problem, named
Approximate Membership Localization (AML). The popular Approximate Membership
Extraction (AME) provides a full coverage to the true matched substrings from a given
document, but many redundancies cause a low efficiency of the AME process and deteriorate the
performance of real-world applications using the extracted substrings.
The AML problem targets at locating non overlapped substrings which is a better approximation
to the true matched substrings without generating overlapped redundancies. In order to perform
AML efficiently, we propose the optimized algorithm P-Prune that prunes a large part of
overlapped redundant matched substrings before generating them.
Our study using several real-word data sets demonstrates the efficiency of P-Prune over a
baseline method. We also study the AML in application to a proposed web-based join framework
scenario which is a search-based approach joining two tables using dictionary-based entity
recognition from web documents. The results not only prove the advantage of AML over AME,
but also demonstrate the effectiveness of our search-based approach.