<p>You can adapt the Sequence-to-Sequence model for NER tagging. Your training text is the source vocabulary/sequences to the encoder:</p>

<pre><code>Yesterday afternoon , Mike Smith drove to New York .
</code></pre>

<p>your BIO / BILOU NER tags are your target vocabulary/sequences to the decoder for NER tagging:</p>

<pre><code>O O O B_PER I_PER O O B_LOC I_LOC O
</code></pre>

<p>or instead use POS tags to the decoder for POS tagging:</p>

<pre><code>NN NN ,  NNP NNP VBD TO NNP NNP . 
</code></pre>

<p>[IMHO using a deep learning approach usually eliminates the need POS tagging as an intermediate step, unless you specifically need those features as an output for something.]</p>

<p>You would probably want to switch off the word embeddings for the decoder.</p>

<p>This well-known paper applies sequence-to-sequence models to syntactic parsing which has some similarities to the POS and/or NER tasks: <a href="http://arxiv.org/abs/1412.7449">Grammar as a Foreign Language</a></p>
TensorFlow with a NER-Tagger
<p>I was wondering if there is any possibility to use Named-Entity-Recognition with a self trained model in tensorflow.</p>

<p>There is a word2vec implementation, but I could not find the 'classic' POS or NER tagger.</p>

<p>Thanks for your help!</p>