I am looking for a Java library to do some initial spell checking / data normalization on user generated text content, imagine the interests entered in a Facebook profile.
This text will be tokenized at some point (before or after spell correction, whatever works better) and some of it used as keys to search for (exact match). It would be nice to cut down misspellings and the like to produce more matches. It would be even better if the correction would perform well on tokens longer than just one word, e.g. “trinking coffee” would become “drinking coffee” and not “thinking coffee”.
I found the following Java libraries for doing spelling correction:
- JAZZY does not seem to be under active development. Also, the dictionary-distance based approach seems inadequate because of the use of non-standard language in social network profiles and multi-word tokens.
- APACHE LUCENE seems to have a statistical spell checker that should be much more suited. Question here would how to create a good dictionary? (We are not using Lucene otherwise, so there is no existing index.)
Any suggestions are welcome!
What you want to implement is not spelling corrector but a fuzzy search. Peter Norvig’s essay is a good starting point to build a fuzzy search from candidates checked against a dictionary.
Alternatively have a look at BK-Trees.
An n-gram index (used by Lucene) produces better results for longer words. The approach to produce candidates up to a given edit distance will probably work good enough for words found in normal text but will not work good enough for names, addresses and scientific texts. It will increase you index size, though.
If you have the texts indexed you have your text corpus (your dictionary). Only what is in your data can be found anyway. You need not use an external dictionary.
A good resource is Introduction to Information Retrieval – Dictionaries and tolerant retrieval . There is a short description of context sensitive spelling correction.