Zamin, N. and Oxley, A. and Abu Bakar, Z. and Farhan, S.A. (2012) A statistical dictionary-based word alignment algorithm: An unsupervised approach. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Malay is categorized as a resource-poor language. Thus, there is limited research on corpus linguistics for Malay. This paper discusses an automated process of applying part-of-speech (POS) tags to Malay words. Conventional tagging works well on static grammatical classes with little ambiguities, as performed in most research on resource-rich languages. However, the grammatical classes of Malay are dynamic, where adjectives can be verbs or adverbs and vice versa. This makes automatic POS tagging of Malay a chaotic and challenging process. There is no labelled data publicly available for Malay while hand-crafted corpora are labour-intensive and time-consuming. Hence, this paper introduces an unsupervised technique to tag Malay terrorism texts as a case study. This is a solution to partially overcome the shortage of annotated resources for Malay and the labour-intensity of a hand-tagged corpus. This approach does not require any labelled training data but involves translation of texts into a resource-rich language, i.e. English, and a dictionary look-up. After comparing the results with human annotators, it is found that the unsupervised technique reaches 76 precision and a 67 recall rate. © 2012 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 9; Conference of 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93334 |
Uncontrolled Keywords: | Automated process; bigram; Corpus linguistics; Dice coefficient; Labour-intensive; malay language; Part of speech tagging; Part-of-speech tags; PoS tagging; Recall rate; Resource-Rich; Training data; Unsupervised approaches; Word alignment, Automation; Information science; Natural language processing systems; Technology, Research |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 15:51 |
Last Modified: | 09 Nov 2023 15:51 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/2781 |