<blockquote>
  <p>does anybody has thoughts on building NER models for labeling text sequences like addresses or temporal expressions?</p>
</blockquote>

<p>Yes: <a href="https://arxiv.org/abs/1606.03475" rel="nofollow noreferrer">https://arxiv.org/abs/1606.03475</a> use RNN for NER.</p>

<p>Figure 1 gives an overview of the ANN architecture:</p>

<p><a href="https://i.stack.imgur.com/o2PFU.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/o2PFU.png" alt="enter image description here"></a></p>
Training NER model to parse temporal and location expressionsions
<p>Folks, does anybody has thoughts on building NER models for labeling text sequences like addresses or temporal expressions?</p>

<p>There is a parser for temporal expressions like "last five days" called SUTime: <a href="http://nlp.stanford.edu/software/sutime.shtml" rel="nofollow">http://nlp.stanford.edu/software/sutime.shtml</a>. Unfortunately, it's buggy and built as massive mash of rules. </p>

<p>Parsing addresses is even more difficult and error prone. CoreNLP parser fails to parse even simple things like Mountain View, CA.</p>

<p>I feel that there should be a way to train RNN to recognize these patterns without maintaining a giant list of rules or a giant lookup table.</p>