No abstract

No abstract

No abstract

Optical Character Recognition (OCR) is a tool in computational technology that allows a recognition of printed characters by manipulating photoelectric devices and computer software. It runs by converting images or texts that are scanned beforehand into machine-readable and editable texts. There are a various numbers of OCR tools in the market for commercial and research use, which are obtainable for free or restrained with purchases. An OCR tool is able to enhance the accuracy of the results which as well relies on pre-processing and subdivision of algorithms. This study intends to investigate the performances of OCR tools in converting the Parliamentary Reports of Hansard Malaysia for developing the Malaysian Hansard Corpus (MHC). By comparing four OCR tools, the study has converted ten reports of Parliamentary Reports which contains a number of 62 pages to see the conversion accuracy and error rate of each conversion tool. In this study, all of the tools are manipulated to convert Adobe Portable Document Format (PDF) files into Plain Text File (txt). The objective of this study is to give an overview based on accuracy and error rate of how each OCR tools essentially works and how it can be utilized to provide assistance towards corpus building. The study indicates that each tool possesses a variety of accuracy and error rates to convert the whole documents from PDF into txt or plain text files. The study proposes that a step of corpus building can be made easier and manageable when a researcher understands the way an OCR tool works in order to choose the best OCR tool prior to the outset of the corpus development.

Removal of stop words is essential in Natural Language Processing and text-related analysis. Existing works on Malay stop words are based on standard Malay and Quranic/Arabic translations into Malay. Thus, there is a lack of domain-specific stop word list, making it discordant for processing of Malay parliamentary discourse. In this paper, we propose a semantic approach towards identifying and removing Malay, conventional Malay spelling and English functional words in analysing a time-series corpus, namely the Malaysian Hansard Corpus (MHC), to extract a Malay specific-domain stop word list. The study utilised a combination of Z-method of most frequently occurring words, words that appear once, and the classic method. The dataset of the corpus evaluated comprised Parliament 1 (year 1959) to Parliament 13 (year 2018). The study then categorised the stop word list according to domainspecific related words. The resulting list comprised 587 stop words. New stop words that emerged from the MHC include parliamentary-related words like 'Berhormat' (salutation to the members of the Parliament), 'Pertua' (salutation to the Speaker of the House), 'ketawa' (laugh) and 'tepuk' (clap). Other than typical English stop words like 'and' and 'the', there are also words like 'hon'ble' (short for 'Honourable') and 'honourable'. The list also includes stop words in conventional Malay spelling like 'untok' (for), 'lebeh' (more), and 'kapada' (to). The proposed set of stop words can be further utilised to assist natural language processing and text analysis.