<p>People do use HMMs to label noun phrases in POS-labeled sentences, but the typical model setup does not work in quite the way you're describing.</p>

<p>Instead, the setup (see <a href="http://arxiv.org/abs/cmp-lg/9807007" rel="nofollow">Chunk tagger-statistical recognition of noun phrases (PDF)</a> and <a href="http://dl.acm.org/citation.cfm?id=1073163" rel="nofollow">Named entity recognition using an HMM-based chunk tagger (PDF)</a> for examples) is to use an HMM with three states:</p>

<ul>
<li>O (not in an NP),</li>
<li>B (beginning of an NP),</li>
<li>I (in an NP, but not the beginning).</li>
</ul>

<p>Each word in a sentence will be assigned one of the states by the HMM. As an example, the sentence:</p>

<blockquote>
  <p>The/DT boy/NN hit/VT the/DT ball/NN with/PP the/DT red/ADJ bat/NN ./.</p>
</blockquote>

<p>might be ideally labeled as follows:</p>

<blockquote>
  <p>The/DT <strong>B</strong> boy/NN <strong>I</strong> hit/VT <strong>O</strong> the/DT <strong>B</strong> ball/NN <strong>I</strong> with/PP <strong>O</strong> the/DT <strong>B</strong> red/ADJ <strong>I</strong> bat/NN <strong>I</strong> ./. <strong>O</strong></p>
</blockquote>

<p>The transitions among these three HMM states can be limited based on prior knowledge of how the sequences will behave; in particular, you can only transition to I from B, but the other transitions are all possible with nonzero probability. You can then use Baum-Welch on a corpus of unlabeled text to train up your HMM (to identify any type of chunk at all -- see <a href="http://www.researchgate.net/publication/220873937_Simple_Unsupervised_Grammar_Induction_from_Raw_Text_with_Cascaded_Finite_State_Models/file/79e4150992f8e85b5b.pdf" rel="nofollow">Simple Unsupervised Grammar Induction from Raw Text with Cascaded Finite State Models (PDF)</a> for an example), or some sort of maximum-likelihood method with a corpus of labeled text (in case you're looking specifically for noun phrases).</p>
Models for classify Noun Phrase?
<p>I need a model for the following tasks:</p>

<p>a sequence of words, with its POS tags. I want to judge whether this sequence of words is a Noun Phrase or not.</p>

<p>One model I can think of is HMM.</p>

<p>For those sequences which are noun phrase, we train a HMM (HMM+). For those are not noun phrase, we try an HMM(HMM-). And when we do prediction for a sequence, we can calculate P(sequence| HMM+) and P(sequence|HMM-). If the former is larger, we think this phrase is a noun phrase, otherwise it's not.</p>

<p>What do you think of it? and do you have any other models suited for this question?  </p>