title: metric learning for information retrieval in semi-structured text?
body: <p>I am interested in parsing semi-structured text. Assuming I have a text with labels of the kind: <code>year_field</code>, <code>year_value</code>, <code>identity_field</code>, <code>identity_value</code>, ..., <code>address_field</code>, <code>address_value</code> and so on.</p>
  
  <p>These fields and their associated values can be everywhere in the text, but usually they are near to each other, and more generally the text in organized in a (very) rough matrix, but rather often the value is just after the associated field with eventually some non-interesting information in between.</p>
  
  <p>The number of different format can be up to several dozens, and is not that rigid (do not count on spacing, moreover some information can be added and removed).</p>
  
  <p>I am looking toward machine learning techniques to extract all those (field,value) of interest.</p>
  
  <p>I think metric learning and/or conditional random fields (<code>CRF</code>) could be of a great help, but I have not practical experience with them.</p>
  
  <p>Does anyone have already encounter a similar problem?</p>
  
  <p>Any suggestion or literature on this topic?</p>