Abstract
In this paper we describe the construction of a spell checker for Sinhala, the language spoken by the majority in Sri Lanka. Due to its morphological richness, the language is difficult to enumerate completely in a lexicon. The approach described is based on n-gram statistics and is relatively inexpensive to construct without deep linguistic knowledge. This approach is particularly useful as there are very few linguistic resources available for Sinhala at present. The proposed algorithm has been shown to be able to detect and correct many of the common spelling errors of the language. Results show a promising performance achieving an average accuracy of 82%. This technique can also be applied to construct spell checkers for other phonetic languages whose linguistic resources are scarce or non-existent. DOI: http://dx.doi.org/10.4038/icter.v3i1.2844ICTer Vol.3 No.1 2010
Highlights
SPELL checking deals with detecting misspelled words in a written text and possibly assisting users in correcting them with the use of a dictionary or otherwise
Spell checkers are widely used in other applications such as Optical Character Recognition (OCR) systems, Automatic Speech Recognition (ASR) systems, Computer Aided Language Learning (CALL) Software, Machine Translation (MT) systems and Text-toSpeech (TTS) systems [1] [2]
These results show an average accuracy of 31.41%, a lower value compared to the Microsoft Office Spell checker
Summary
SPELL checking deals with detecting misspelled words in a written text and possibly assisting users in correcting them with the use of a dictionary or otherwise. Spell checkers are well-known components of word-processing applications. Spell checkers are widely used in other applications such as Optical Character Recognition (OCR) systems, Automatic Speech Recognition (ASR) systems, Computer Aided Language Learning (CALL) Software, Machine Translation (MT) systems and Text-toSpeech (TTS) systems [1] [2]. The history of automatic spelling correction goes back to the 1960s [3]. Even after decades of extensive research and development, the effectiveness of spell checkers remains a challenge today. Common spelling mistakes can be classified into two broad categories: 1) non-word errors, where the word itself
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