Onyenwe, I.E., Hepple, M., Chinedu, U. et al. (1 more author) (2019) Toward an effective Igbo part-of-speech tagger. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 18 (4). ISSN 2375-4699
Abstract
Part-of-speech (POS) tagging is a well-established technology for most Western European languages and a few other world languages, but it has not been evaluated on Igbo, an agglutinative African language. This article presents POS tagging experiments conducted using an Igbo corpus as a test bed for identifying the POS taggers and the Machine Learning (ML) methods that can achieve a good performance with the small dataset available for the language. Experiments have been conducted using different well-known POS taggers developed for English or European languages, and different training data styles and sizes. Igbo has a number of language-specific characteristics that present a challenge for effective POS tagging. One interesting case is the wide use of verbs (and nominalizations thereof) that have an inherent noun complement, which form “linked pairs” in the POS tagging scheme, but which may appear discontinuously. Another issue is Igbo’s highly productive agglutinative morphology, which can produce many variant word forms from a given root. This productivity is a key cause of the out-of-vocabulary (OOV) words observed during Igbo tagging. We report results of experiments on a promising direction for improving tagging performance on such morphologically-inflected OOV words.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. |
Keywords: | Natural language processing (NLP); language technology; corpus annotation; part-of-speech (POS) tagging; POS tagger; text processing; African language; Igbo; corpora; morphological analysis; machine learning; tagset |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Sep 2019 08:22 |
Last Modified: | 24 Sep 2019 08:22 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | 10.1145/3314942 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151188 |