I'm wondering what the best approach would be in this somewhat unique case. My data is a fairly flat table of classification data, where each column gets increasingly specific, down to the exact item i'm looking for. Think the biology tree, where each column would be Kingdom, Order, Family, Genus, etc.. down to the exact animal. In this analogy, each animal (or item in my case) would be a row in the CSV I'm importing.
Trick is, my input is spoken word. I'm using a dictation tool to get my input values, so it wont be a traditional keyword-like input. So far, I've heard good things about OpenNLP, Snowball, and UIMA. I'd like to open this up to a larger community though. Which utility would be best for matching spoken word against classification data? -- View this message in context: http://lucene.472066.n3.nabble.com/Natural-Language-Processing-on-input-tp4046772.html Sent from the Solr - User mailing list archive at Nabble.com.
