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Neural Networks for Wordform Recognition
Number of Authors: 2
1994 (English)Report (Refereed)
Abstract [en]

The paper outlines a method for automatic lexical acquisition using three-layered back-propagation networks. Several experiments have been carried out where the performance of different network architectures have been compared to each other on two tasks: overall part-of-speech (noun, adjective or verb) classification and classification by a set of 13 possible output categories. The best results for the simple task were obtained by networks consisting of 204-212 input neurons and 40 hidden-layer neurons, reaching a classification rate of 93.6%. The best result for the more complex task was 96.4%, which was achieved by a net with 423 input neurons and 80 hidden-layer neurons. These results are rather promising and the paper compares them to the performance reported by rule-based and purely statistical methods; a comparison that shows the neural network completely compatible with the statistical approach. The rule-based method is, however, still better, even though it should noted that the task that the rule-based system performs is somewhat different from that of the neural net.

Place, publisher, year, edition, pages
Swedish Institute of Computer Science , 1994, 1. , 32 p.
Series
SICS Research Report, ISSN 0283-3638 ; R94:05
Keyword [en]
Back-propagation neural networks. Lexical acquisition
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-21379OAI: oai:DiVA.org:ri-21379DiVA: diva2:1041415
Available from: 2016-10-31 Created: 2016-10-31Bibliographically approved

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