I am researching ways to classify words in text and I'm wondering what options there are and which are best suited to this job. I'm mostly interested in keywords which are most often nouns.
So far I know I could use a Bayes classifier, blacklist, or whitelist. However, I haven't had the best of luck with each of these so far.
Next I tried building a blacklist to match everything except known stop-words, verbs, and such. However, It would take a long investment of time to build a list large enough to handle the 200,000 known English (let alone other languages) words. In addition, keeping that many words in memory for comparison is not practical for performance on commodity hardware.
Using I have had luck with a bayes classifier matching spam and sentiment, but I'm not sure how to use it to tell nouns (or keywords) from other words since there are so many similarities between all the groups of words.
Last, I tried using regex to spot keywords based on proper nouns and names since they are always capitalized in English. The problem is that many keywords are not capitalized resulting in lacking results.
What other options exist for classifying keywords? What other design patterns could I use with the options above for better results?