Abstract
The algorithm that has been proposed in this paper tries to segregate words from various languages (namely Hindi, English, Bengali and Gujarati) and provide relevant replacements for the misspelled or unknown words in a given query. Thus, generating a relevant query in which the original language of each word is known. First, the words are matched directly with the dictionaries of each language transliterated into English. And then, for those that do not match, a set of probable words from all the dictionaries taking words that are closest to the given spelling is shortlisted using the Levenshtein algorithm. After this, to achieve a higher level of generalization, we use a list of probabilities of doublets and triplets of words occurring together that are computed from a training database. The probabilities computed further determine the relevance of those words in the given text allowing us to pick the most relevant match.
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Gupta, B., Bhatt, G., Mittal, A. (2016). Language Identification and Disambiguation in Indian Mixed-Script. In: Bjørner, N., Prasad, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2016. Lecture Notes in Computer Science(), vol 9581. Springer, Cham. https://doi.org/10.1007/978-3-319-28034-9_14
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