WNS pell: a W ord N et-Based Spell Corrector (2016)

Abstract: This paper presents a standalone spell corrector, WNSpell, based on and written for WordNet. It is aimed at generating the best possible suggestion for a mistyped query but can also serve as an all-purpose spell corrector. The spell corrector consists of a standard initial correction system, which evaluates word entries using a multifaceted approach to achieve the best results, and a semantic recognition system, wherein given a related word input, the system will adjust the spelling suggestions accordingly. Both feature significant performance improvements over current context-free spell correctors.

## introduction wordnet is a lexical database of english words and serves as the premier tool for word sense disambiguation. it stores around 160,000 word forms, or lemmas, and 120,000 word senses, or synsets, in a large graph of semantic relations. the goal of this paper is to introduce a spell corrector for the wordnet interface, directed at correcting queries and aiming to take advantage of word-net's structure. ## semantic input: the second part of the spell corrector adds a semantic aspect into the correction of the search query. when users have trouble entering the query and cannot immediately choose a suggested correction, they are given the option to enter a semantically related word. wnspell then takes this word into account when generating suggestions, harnessing wordnet's vast semantic network to further optimize results. this added dimension in spell correction is very helpful for the more severe errors, which usually arise from the "@xmath0. these are much harder to deal with than conventional mistypings or misspellings, and are exactly the type of error wnspell needs to be able to handle (as mistyped or even misspelled queries can be fixed without too much trouble by the user). the semantic anchor the related word provides helps wnspell establish the idea" behind the desired word and thus refine the suggestions for the desired word. to incorporate the related word into the suggestion generation, we add some modifications to the original context-free spell corrector. ## results we used the aspell data set to train the system. the test set consists of 547 hard-to-correct words. this is ideal for our purposes, as we are focusing on correcting bad misspellings as well as the easy ones. most of the empirically-derived constants from (3.2) were determined based off of results from this data set. ## conclusions: the wnspell algorithm introduced in this paper presents a significant improvement in accuracy in correcting standalong spelling corrections over other systems, including the most recent version of aspell and other commercially used spell correctors such as huspell and word, by approximately 20%. wnspell is able to take into a variety of factors regarding different types of spelling errors and using a carefully tuned algorithm to account for much of the diversity in spelling errors presented in the test data sets. there is a efficient sample space pruning system that restricts the number of words to be considered, strongly improved by a phonetic key, and an accurate scoring system that then compares the words. the accuracy of wn-spell in correcting hard-to-correct words is quite close that of most peoples' abilities and significantly stronger than other methods. wnspell also provides an alternative using a related word to help the system find the desired correction even if the user is far off the mark in terms of spelling or phonetics. this added feature once again significantly increases the accuracy of wnspell by approximately 10% by directly connecting the idea word the user has in mind to the word itself. this link allows for the possibility of users who only know what rough meaning their desired word has or context it is in to actually find the word.