Part-of-speech tagger for Malay social media texts
Processing the meaning of words in social media texts, such as tweets, is challenging in natural language processing. Malay tweets are no exception because they demonstrate distinct linguistic phenomena, such as the use of dialects from each state in Malaysia; borrowing foreign language terms in...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2018
|
| Online Access: | http://journalarticle.ukm.my/17663/ http://journalarticle.ukm.my/17663/1/28357-89214-1-PB.pdf |
| _version_ | 1848814369643692032 |
|---|---|
| author | Siti Noor Allia Noor Ariffin, Sabrina Tiun, |
| author_facet | Siti Noor Allia Noor Ariffin, Sabrina Tiun, |
| author_sort | Siti Noor Allia Noor Ariffin, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Processing the meaning of words in social media texts, such as tweets, is challenging in
natural language processing. Malay tweets are no exception because they demonstrate distinct
linguistic phenomena, such as the use of dialects from each state in Malaysia; borrowing
foreign language terms in the context of Malay language; and using mixed languages,
abbreviations and spelling errors or mistakes in sentence structure. Tagging the word class of
tweets is an arduous task because tweets are characterised by their distinctive style, linguistic
sounds and errors. Currently, existing works on Malay part-of-speech (POS) are based only
on standard Malay and formal texts and are thus unsuitable for tagging tweet texts. Thus, a
POS model of tweet tagging for non-standardised Malay language must be developed. This
study aims to design and implement a non-standardised Malay POS model for tweets and
performs assessment on the basis of the word tagging accuracy of test data of unnormalised
and normalised tweet texts. A solution that adopts a probabilistic POS tagging called QTAG
is proposed. Results show that the Malay QTAG achieves best average POS tagging
accuracies of 90% and 88.8% for normalised and unnormalised test datasets, respectively. |
| first_indexed | 2025-11-15T00:33:00Z |
| format | Article |
| id | oai:generic.eprints.org:17663 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:33:00Z |
| publishDate | 2018 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:176632021-11-24T00:47:45Z http://journalarticle.ukm.my/17663/ Part-of-speech tagger for Malay social media texts Siti Noor Allia Noor Ariffin, Sabrina Tiun, Processing the meaning of words in social media texts, such as tweets, is challenging in natural language processing. Malay tweets are no exception because they demonstrate distinct linguistic phenomena, such as the use of dialects from each state in Malaysia; borrowing foreign language terms in the context of Malay language; and using mixed languages, abbreviations and spelling errors or mistakes in sentence structure. Tagging the word class of tweets is an arduous task because tweets are characterised by their distinctive style, linguistic sounds and errors. Currently, existing works on Malay part-of-speech (POS) are based only on standard Malay and formal texts and are thus unsuitable for tagging tweet texts. Thus, a POS model of tweet tagging for non-standardised Malay language must be developed. This study aims to design and implement a non-standardised Malay POS model for tweets and performs assessment on the basis of the word tagging accuracy of test data of unnormalised and normalised tweet texts. A solution that adopts a probabilistic POS tagging called QTAG is proposed. Results show that the Malay QTAG achieves best average POS tagging accuracies of 90% and 88.8% for normalised and unnormalised test datasets, respectively. Penerbit Universiti Kebangsaan Malaysia 2018-11 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17663/1/28357-89214-1-PB.pdf Siti Noor Allia Noor Ariffin, and Sabrina Tiun, (2018) Part-of-speech tagger for Malay social media texts. GEMA: Online Journal of Language Studies, 18 (4). pp. 124-142. ISSN 1675-8021 https://ejournal.ukm.my/gema/issue/view/1146 |
| spellingShingle | Siti Noor Allia Noor Ariffin, Sabrina Tiun, Part-of-speech tagger for Malay social media texts |
| title | Part-of-speech tagger for Malay social media texts |
| title_full | Part-of-speech tagger for Malay social media texts |
| title_fullStr | Part-of-speech tagger for Malay social media texts |
| title_full_unstemmed | Part-of-speech tagger for Malay social media texts |
| title_short | Part-of-speech tagger for Malay social media texts |
| title_sort | part-of-speech tagger for malay social media texts |
| url | http://journalarticle.ukm.my/17663/ http://journalarticle.ukm.my/17663/ http://journalarticle.ukm.my/17663/1/28357-89214-1-PB.pdf |