How to recognize words in text with non-word tokens?

I am currently parsing a bunch of mails and want to get wor开发者_如何学运维ds and other interesting tokens out of mails (even with spelling errors or combination of characters and letters, like "zebra21" or "customer242"). But how can I know that "0013lCnUieIquYjSuIA" and "anr5Brru2lLngOiEAVk1BTjN" are not words and not relevant? How to extract words and discard tokens that are encoding errors or parts of pgp signature or whatever else we get in mails and know that we will never be interested in those?

You need to decide on a good enough criteria for a word and write a regular expression or a manual to enforce it.
A few rules that can be extrapolated from your examples:

• words can start with a captial letter or be all capital letters but if you have more than say, 2 uppercase letters and more than 2 lowercase letters inside a word, it's not a word
• If you have numbers inside the word, it's not a word
• if it's longer than say, 20 characters

There's no magic trick. you need to decide what you want the rules to be and make them happen.

Al alternative way is to train some kind of Hidden Markov-Models system to recognize things that sound like words but I think this is an overkill for what you want to do.

http://en.wikipedia.org/wiki/English_words_with_uncommon_properties
you can make rules that reject anything with these 'uncommon properties' to build a system that accepts most actual words

Although I generally agree with shoosh's answer, his approach makes it easy to achieve high recall but also low precision, i.e. you would get almost all real words but also a lot non-words. If your definition of word is too restrictive, it's the other way around but that's also not what you want since then you would miss cases like 'zebra123'. So here are a few ideas about how to improve precision:

1. It may be worthwile thinking about if you could determine what parts of an email belong to the main text and which are footers like pgp signatures. I'm sure it's possible to find some simple heuristics that match most cases, e.g. cut of everything below a line which consists only of '-'-characters.

2. Depending on your performance criteria you may want to check if a word is a real word or contains a real word by matching against a simple word list. It's easy to find quite exhaustive lists of Englisch words on the web, and you could also compile one yourself by extracting words from a large and clean text corpus.

3. Using a lexical analyser, you could filter every token which is marked as unknown.

4. Some simple statistics may tell you how likely it is that something is a word. Tokens which occur with high frequency most probably are words. Tokens which appear only once or whose number is below a certain threshold very probably are not words. Common spelling errors should appear more than once and uncommon ones may be ignored.

Some if these suggestions clearly don't work for cases like 'zebra123'. Again, simply cutting off, or splitting on, in-word numbers may do the trick.

My general approach would be to first identify tokens which certainly are words (using the suggestions above), then identify tokens which certainly are not words (using a regular expression), and then look (with your eyes) at the few hundred or thousand remaining tokens to find common characteristics to handle these separately.