Towards a Malay derivational lexicon: learning affixes using expectation maximization

We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of morphological segmentations produced by a na¨ıve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given segmentation. We train the...

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Main Authors:	Sulaiman, Suriani, Gasser, Michael, Kubler, Sandra
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/32082/ http://irep.iium.edu.my/32082/ http://irep.iium.edu.my/32082/1/W11-3005.pdf

id	iium-32082
recordtype	eprints
spelling	iium-320822013-12-26T03:12:22Z http://irep.iium.edu.my/32082/ Towards a Malay derivational lexicon: learning affixes using expectation maximization Sulaiman, Suriani Gasser, Michael Kubler, Sandra QA75 Electronic computers. Computer science We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of morphological segmentations produced by a na¨ıve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given segmentation. We train the model with EM to find the segmentation that maximizes the probability of each morpheme. We extract the set of affix patterns produced by our algorithm and evaluate them against two references: a list of affix patterns extracted from our hand-segmented derivational wordlist and a derivational history produced by a stemmer. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/32082/1/W11-3005.pdf Sulaiman, Suriani and Gasser, Michael and Kubler, Sandra (2011) Towards a Malay derivational lexicon: learning affixes using expectation maximization. In: 2nd Workshop on South and Souteast Asian Natural Language Processing (WSSANLP), IJCNLP 2011, 8th-13th Nov. 2011, Chiang Mai, Thailand. http://aclweb.org/anthology//W/W11/W11-3005.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Sulaiman, Suriani Gasser, Michael Kubler, Sandra Towards a Malay derivational lexicon: learning affixes using expectation maximization
description	We propose an unsupervised training method to guide the learning of Malay derivational morphology from a set of morphological segmentations produced by a na¨ıve morphological analyzer. Using a morphology-based language model, we first estimate the probability of a given segmentation. We train the model with EM to find the segmentation that maximizes the probability of each morpheme. We extract the set of affix patterns produced by our algorithm and evaluate them against two references: a list of affix patterns extracted from our hand-segmented derivational wordlist and a derivational history produced by a stemmer.
format	Conference or Workshop Item
author	Sulaiman, Suriani Gasser, Michael Kubler, Sandra
author_facet	Sulaiman, Suriani Gasser, Michael Kubler, Sandra
author_sort	Sulaiman, Suriani
title	Towards a Malay derivational lexicon: learning affixes using expectation maximization
title_short	Towards a Malay derivational lexicon: learning affixes using expectation maximization
title_full	Towards a Malay derivational lexicon: learning affixes using expectation maximization
title_fullStr	Towards a Malay derivational lexicon: learning affixes using expectation maximization
title_full_unstemmed	Towards a Malay derivational lexicon: learning affixes using expectation maximization
title_sort	towards a malay derivational lexicon: learning affixes using expectation maximization
publishDate	2011
url	http://irep.iium.edu.my/32082/ http://irep.iium.edu.my/32082/ http://irep.iium.edu.my/32082/1/W11-3005.pdf
first_indexed	2023-09-18T20:46:17Z
last_indexed	2023-09-18T20:46:17Z
_version_	1777409696441827328

Towards a Malay derivational lexicon: learning affixes using expectation maximization

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