Tagging documents using neural networks based on local word features

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings of the International Joint Conference on Neural Networks

First Page

724

Last Page

731

Publication Date

1-1-2014

Abstract

Keywords and key-phrases that concisely represent text documents are integral to many knowledge management and text information retrieval systems, as well as digital libraries in general. Not all text documents, however, are annotated with good keywords; and the quality of these keywords is often dependent on a tedious, sometimes manual, extraction and tagging process. To automatically extract high quality keywords without the need for a semantic analysis of the document, it is shown that artificial neural networks (ANN) can be trained to only consider in-document word features such as word frequency, word distribution in document, use of word in special parts of the document, and use of word formatting features (i.e. bold-faced, italicized, large-font size). Results show that purely local features are adequate in determining whether a word in a document is a keyword or not. Classification performance yields a G mean of a least 0.83, and weighted f-measure of 0.96 for both keywords and non-keywords. Precision for keywords alone, however, is not as high. To understand the basis for classifying keywords, C4.5 is used to extract rules from the ANN. The extracted rules from C4.5, in the form of a decision tree, show the relative importance of the different document features that were extracted. © 2014 IEEE.

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Digitial Object Identifier (DOI)

10.1109/IJCNN.2014.6889456

Disciplines

Software Engineering

Keywords

Automatic indexing; Text processing (Computer science); Neural networks (Computer science)

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