<p>If you are looking for a fast Java (or Python) POS tagger, you might consider to use <a href="http://rdrpostagger.sourceforge.net/" rel="nofollow">RDRPOSTagger</a>. RDRPOSTagger is a robust, easy-to-use and language-independent toolkit for POS and morphological tagging. It  obtains fast performance in both learning and tagging process. For example in Java, tagging speed is 90K English words/second using a computer with Core2Duo 2.4 GHz. And it achieves a very competitive accuracy in comparison to the state-of-the-art results. See experimental results including performance speed and tagging accuracy on 13 languages in <a href="http://arxiv.org/abs/1412.4021" rel="nofollow">this paper</a>.</p>
POS Tagging too slow - using OpenNLP
<p>I am just playing around with Part-of-speech Tagging, and started using OpenNLP. </p>

<p>I am using the following code to load the model (Java):</p>

<pre><code>        m_modelFile = new FileInputStream("c:\\DATA\\en-parser-chunking.bin");
        m_model = new ParserModel(m_modelFile);
        m_parser = ParserFactory.create(m_model);  
        ...
        Parse topParses[] = ParserTool.parseLine(sentence, m_parser, 1);
</code></pre>

<p>I am noticing that the call to create the ParserModel object is insanely slow. Could be b/c en-parser-chunking.bin is 35MB in size. Is there a better way to use this so that it's not this slow? Alternatively, is there a POS tagger you recommend or a way of calling the API that's faster?</p>

<p>I've been playing around with the accuracy, and it's pretty good. But, I am not happy with the performance when loading the model...</p>

<p>Thanks guys.</p>