THere are a bunch of papers on this. Search "named entity recognizer CRF" on google.
The basic idea is that an HMM or CRF has internal state that can be used to mark named entities. We don't have to define what the hidden states mean, just help the HMM or CRF find an internal representation that has the right outputs. The basic idea is that the text and text characteristics are inputs that are applied at each successive word position. The internal state is, well, internal, but it triggers observable transitions in the output which are the label that we want to have. For NER, this label is typically something like NAMED_ENTITY / NORMAL_TEXT or something more refined. On Tue, Jan 24, 2012 at 3:55 AM, Paritosh Ranjan <pran...@xebia.com> wrote: > If you can also also guide a bit on how to use HMM or CRF for this problem > ( on a high level ), then that would be of great help too. >
