A Neural Network Based Dutch Part of Speech Tagger

Mannes  Poel; Egwin Boschman; Rieks op den Akker

A Neural Network Based Dutch Part of Speech Tagger

Mannes Poel, Egwin Boschman, Rieks op den Akker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

645 Downloads (Pure)

Abstract

In this paper a Neural Network is designed for Part-of-Speech Tagging of Dutch text. Our approach uses the Corpus Gesproken Nederlands (CGN) consisting of almost 9 million transcribed words of spoken Dutch, divided into 15 different categories. The outcome of the design is a Neural Network with an input window of size 8 (4 words back and 3 words ahead) and a hidden layer of 370 neurons. The words ahead are coded based on the relative frequency of the tags in the training set for the word. Special attention is paid to unknown words (words not in the training set) for which such a relative frequency cannot be determined. Based on a 10-fold cross validation an approximation of the relative frequency of tags for unknown words is determined. The performance of the Neural Network is 97.35%, 97.88% on known words and 41.67% on unknown words. This is comparable to state of the art performances found in the literature. The special coding of unknown words resulted of an increase of almost 13% for the tagging of unknown words.

Original language	English
Title of host publication	BNAIC 2008
Subtitle of host publication	Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008
Editors	Anton Nijholt, Maja Pantic, Mannes Poel, Hendri Hondorp
Place of Publication	Enschede
Publisher	Twente University Press (TUP)
Pages	217-224
Number of pages	8
Publication status	Published - 2008
Event	20th Benelux Conference on Artificial Intelligence, BNAIC 2008 - Boekelo, Netherlands Duration: 30 Oct 2008 → 31 Oct 2008 Conference number: 20

Publication series

Name	BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference
Publisher	Twente University Press
Number	20
ISSN (Print)	1568-7805

Conference

Conference	20th Benelux Conference on Artificial Intelligence, BNAIC 2008
Abbreviated title	BNAIC
Country/Territory	Netherlands
City	Boekelo
Period	30/10/08 → 31/10/08

Keywords

IR-65237
METIS-255028
EWI-14662

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Poel2008neuralFinal published version, 102 KB

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Poel, M., Boschman, E., & op den Akker, R. (2008). A Neural Network Based Dutch Part of Speech Tagger. In A. Nijholt, M. Pantic, M. Poel, & H. Hondorp (Eds.), BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008 (pp. 217-224). (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; No. 20). Twente University Press (TUP).

Poel, Mannes ; Boschman, Egwin ; op den Akker, Rieks. / A Neural Network Based Dutch Part of Speech Tagger. BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. editor / Anton Nijholt ; Maja Pantic ; Mannes Poel ; Hendri Hondorp. Enschede : Twente University Press (TUP), 2008. pp. 217-224 (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; 20).

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title = "A Neural Network Based Dutch Part of Speech Tagger",

abstract = "In this paper a Neural Network is designed for Part-of-Speech Tagging of Dutch text. Our approach uses the Corpus Gesproken Nederlands (CGN) consisting of almost 9 million transcribed words of spoken Dutch, divided into 15 different categories. The outcome of the design is a Neural Network with an input window of size 8 (4 words back and 3 words ahead) and a hidden layer of 370 neurons. The words ahead are coded based on the relative frequency of the tags in the training set for the word. Special attention is paid to unknown words (words not in the training set) for which such a relative frequency cannot be determined. Based on a 10-fold cross validation an approximation of the relative frequency of tags for unknown words is determined. The performance of the Neural Network is 97.35%, 97.88% on known words and 41.67% on unknown words. This is comparable to state of the art performances found in the literature. The special coding of unknown words resulted of an increase of almost 13% for the tagging of unknown words.",

keywords = "IR-65237, METIS-255028, EWI-14662",

author = "Mannes Poel and Egwin Boschman and {op den Akker}, Rieks",

note = "http://eprints.ewi.utwente.nl/14662 ; 20th Benelux Conference on Artificial Intelligence, BNAIC 2008, BNAIC ; Conference date: 30-10-2008 Through 31-10-2008",

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publisher = "Twente University Press (TUP)",

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Poel, M, Boschman, E & op den Akker, R 2008, A Neural Network Based Dutch Part of Speech Tagger. in A Nijholt, M Pantic, M Poel & H Hondorp (eds), BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference, no. 20, Twente University Press (TUP), Enschede, pp. 217-224, 20th Benelux Conference on Artificial Intelligence, BNAIC 2008, Boekelo, Netherlands, 30/10/08.

A Neural Network Based Dutch Part of Speech Tagger. / Poel, Mannes ; Boschman, Egwin; op den Akker, Rieks.
BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. ed. / Anton Nijholt; Maja Pantic; Mannes Poel; Hendri Hondorp. Enschede: Twente University Press (TUP), 2008. p. 217-224 (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; No. 20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - In this paper a Neural Network is designed for Part-of-Speech Tagging of Dutch text. Our approach uses the Corpus Gesproken Nederlands (CGN) consisting of almost 9 million transcribed words of spoken Dutch, divided into 15 different categories. The outcome of the design is a Neural Network with an input window of size 8 (4 words back and 3 words ahead) and a hidden layer of 370 neurons. The words ahead are coded based on the relative frequency of the tags in the training set for the word. Special attention is paid to unknown words (words not in the training set) for which such a relative frequency cannot be determined. Based on a 10-fold cross validation an approximation of the relative frequency of tags for unknown words is determined. The performance of the Neural Network is 97.35%, 97.88% on known words and 41.67% on unknown words. This is comparable to state of the art performances found in the literature. The special coding of unknown words resulted of an increase of almost 13% for the tagging of unknown words.

AB - In this paper a Neural Network is designed for Part-of-Speech Tagging of Dutch text. Our approach uses the Corpus Gesproken Nederlands (CGN) consisting of almost 9 million transcribed words of spoken Dutch, divided into 15 different categories. The outcome of the design is a Neural Network with an input window of size 8 (4 words back and 3 words ahead) and a hidden layer of 370 neurons. The words ahead are coded based on the relative frequency of the tags in the training set for the word. Special attention is paid to unknown words (words not in the training set) for which such a relative frequency cannot be determined. Based on a 10-fold cross validation an approximation of the relative frequency of tags for unknown words is determined. The performance of the Neural Network is 97.35%, 97.88% on known words and 41.67% on unknown words. This is comparable to state of the art performances found in the literature. The special coding of unknown words resulted of an increase of almost 13% for the tagging of unknown words.

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KW - METIS-255028

KW - EWI-14662

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A2 - Pantic, Maja

A2 - Poel, Mannes

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ER -

Poel M, Boschman E, op den Akker R. A Neural Network Based Dutch Part of Speech Tagger. In Nijholt A, Pantic M, Poel M, Hondorp H, editors, BNAIC 2008: Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, Enschede/Bad Boekelo, October 30-31, 2008. Enschede: Twente University Press (TUP). 2008. p. 217-224. (BNAIC: proceedings of the ... Belgium/Netherlands Artificial Intelligence Conference; 20).

A Neural Network Based Dutch Part of Speech Tagger

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