# Dealing with lots of non-dictionary words **URL:** \ **Category:** Elasticsearch\ **Created:** [March 28, 2016, 7:37am UTC](https://discuss.elastic.co/t/dealing-with-lots-of-non-dictionary-words/45563 "2016-03-28T07:37:03Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![BenSeb](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/benseb/32/8714_2.png) [@BenSeb](https://discuss.elastic.co/u/BenSeb)\ **Post date:** [March 28, 2016, 7:37am UTC](https://discuss.elastic.co/t/dealing-with-lots-of-non-dictionary-words/45563/1 "2016-03-28T07:37:03Z")
Hi We're just starting to implement ES, moving over from Sphinx One of the big issues we currently face, is dealing with misspellings. However, in our realm, lots of our documents contain branded words which are not in the dictionary For example, names of artists, brands, etc. It's almost a reverse spell check _ **"corruption" =\> Did you mean "Korruption?"** _ (where Korruption is indexed highly in our documents) Because some of these are misspelt by the users when searching,we want to suggest the correct spelling, but this must be done according to indexed documents, rather than dictionary terms. I've been looking at [https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-phrase.html](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters-phrase.html) and hoping this will work, perhaps setting \*\* real\_word\_error\_likelihood\*\* lower? Any other suggestions on the best implementation would be very welcome Thanks ---
**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\ **Post date:** [July 5, 2017, 11:04pm UTC](https://discuss.elastic.co/t/dealing-with-lots-of-non-dictionary-words/45563/2 "2017-07-05T23:04:46Z")