“Uttam”

Abstract

In this article, we propose a system called “UTTAM,” for correcting spelling errors in Hindi language text using supervised learning. Unlike other languages, Hindi contains a large set of characters, words with inflections and complex characters, phonetically similar sets of characters, and so on. The complexity increases the possibility of confusion and occasionally leads to entering a wrong character in a word. The existence of spelling errors in text significantly decreases the accuracy of the available resources, like search engine, text editor, and so on. The proposed work is the first approach to correct non-word (Out of Vocabulary) errors as well as real-word errors simultaneously in a sentence of Hindi language. The proposed method investigates the human behavior, i.e., the type and frequency of spelling errors done by humans in Hindi text. Based on the type and frequency of spelling errors, the heterogeneous data is collected in matrices. This data in matrices is used to generate the suitable candidate words for an input word. After generating candidate words, the Viterbi algorithm is applied to perform the word correction. The Viterbi algorithm finds the best sequence of candidate words to correct the input sentence. For Hindi, this work is the first attempt for real-word error correction. For non-word errors, the experiments show that “UTTAM” performs better than the existing systems SpellGuru and Saksham.

Code‐mixed Hindi‐English text correction using fuzzy graph and word embedding

“…or adverb then we need to skip these words as these semantic relations are not applicable on adjective and adverb. Table 11 shows the comparative results with Etoori et al (2018) and n-gram based Viterbi algorithm by Jain et al (2018). Proposed approach clearly outperforms the both state-of-art methods.…”

“…Whilst generating our closure graph , the system used hypernymy, hyponymy, meronymy, holonymy and similarity of the words of suggestive list but if we encounter an adjective or adverb then we need to skip these words as these semantic relations are not applicable on adjective and adverb. Table 11 shows the comparative results with Etoori et al (2018) and n‐gram based Viterbi algorithm by Jain et al (2018). Proposed approach clearly outperforms the both state‐of‐art methods.…”

Spell corrector for Bangla language using Norvig’s algorithm and Jaro-Winkler distance

“…When measured the performance of their proposed system over other existing approaches, they got the highest accuracy of 85.4% whereas others have the 77.6% in case of Hindi Language. Jain et al [24] proposed a method of detecting single word OOV or real word error where consists three main steps. First the data was collected in a confusion matrix which was used to explore frequency and types of error that had been occurred.…”