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 <a href="#func-members">Functions</a>  </div>
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 <div class="title">crf_feature_gen.sql_in File Reference</div>  </div>
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 <div class="contents">

 <p>SQL function for POS/NER feature extraction.
 <a href="#details">More...</a></p>
 <table class="memberdecls">
 <tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
 Functions</h2></td></tr>
 <tr class="memitem:a3548bd640c2af456f3755cb24b913ebd"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_train_fgen</a> (text train_segment_tbl, text regex_tbl, text label_tbl, text dictionary_tbl, text train_feature_tbl, text train_featureset_tbl)</td></tr>
 <tr class="memdesc:a3548bd640c2af456f3755cb24b913ebd"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function extracts POS/NER features from the training data.  <a href="#a3548bd640c2af456f3755cb24b913ebd">More...</a><br /></td></tr>
 <tr class="separator:a3548bd640c2af456f3755cb24b913ebd"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ad9af2c3d1a4f1c937795634ca297dede"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_test_fgen</a> (text test_segment_tbl, text dictionary_tbl, text label_tbl, text regex_tbl, text crf_weights_tbl, text viterbi_mtbl, text viterbi_rtbl)</td></tr>
 <tr class="memdesc:ad9af2c3d1a4f1c937795634ca297dede"><td class="mdescLeft">&#160;</td><td class="mdescRight">This function extracts POS/NER features from the testing data.  <a href="#ad9af2c3d1a4f1c937795634ca297dede">More...</a><br /></td></tr>
 <tr class="separator:ad9af2c3d1a4f1c937795634ca297dede"><td class="memSeparator" colspan="2">&#160;</td></tr>
 </table>
 <a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
 <div class="textblock"><dl class="section date"><dt>Date</dt><dd>February 2012</dd></dl>
 <dl class="section see"><dt>See also</dt><dd>For an introduction to POS/NER feature extraction, see the module description <a class="el" href="group__grp__crf.html">Conditional Random Field</a> </dd></dl>
 </div><h2 class="groupheader">Function Documentation</h2>
 <a id="ad9af2c3d1a4f1c937795634ca297dede"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#ad9af2c3d1a4f1c937795634ca297dede">&#9670;&nbsp;</a></span>crf_test_fgen()</h2>

 <div class="memitem">
 <div class="memproto">
       <table class="memname">
         <tr>
           <td class="memname">void crf_test_fgen </td>
           <td>(</td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>test_segment_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>dictionary_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>label_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>regex_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>crf_weights_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>viterbi_mtbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>viterbi_rtbl</em>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
         </tr>
       </table>
 </div><div class="memdoc">
 <p>This feature extraction function will produce two factor tables, "m table" (<em>viterbi_mtbl</em>) and "r table" (<em>viterbi_rtbl</em>). The <em>viterbi_mtbl</em> table and <em>viterbi_rtbl</em> table are used to calculate the best label sequence for each sentence.</p>
 <ul>
 <li><em>viterbi_mtbl</em> table encodes the edge features which are solely dependent on upon current label and previous y value. The m table has three columns which are prev_label, label, and value respectively. If the number of labels in \( n \), then the m factor table will \( n^2 \) rows. Each row encodes the transition feature weight value from the previous label to the current label.</li>
 </ul>
 <p><em>startFeature</em> is considered as a special edge feature which is from the beginning to the first token. Likewise, <em>endFeature</em> can be considered as a special edge feature which is from the last token to the very end. So m table encodes the edgeFeature, startFeature, and endFeature. If the total number of labels in the label space is 45 from 0 to 44, then the m factor array is as follows: </p><pre>
                  0  1  2  3  4  5...44
 startFeature -1  a  a  a  a  a  a...a
 edgeFeature   0  a  a  a  a  a  a...a
 edgeFeature   1  a  a  a  a  a  a...a
 ...
 edgeFeature  44  a  a  a  a  a  a...a
 endFeature   45  a  a  a  a  a  a...a</pre><ul>
 <li>viterbi_r table is related to specific tokens. It encodes the single state features, e.g., wordFeature, RegexFeature for all tokens. The r table is represented in the following way. <pre>
        0  1  2  3  4...44
 token1 a  a  a  a  a...a
 token2 a  a  a  a  a...a</pre></li>
 </ul>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramname">test_segment_tbl</td><td>Name of table containing all the tokenized testing sentences. </td></tr>
     <tr><td class="paramname">dictionary_tbl</td><td>Name of table containing the dictionary_tbl. </td></tr>
     <tr><td class="paramname">label_tbl</td><td>Name of table containing the the label space used in POS or other NLP tasks. </td></tr>
     <tr><td class="paramname">regex_tbl</td><td>Name of table containing all the regular expressions to capture regex features. </td></tr>
     <tr><td class="paramname">crf_weights_tbl</td><td>Name of the table containing featureset weights. </td></tr>
     <tr><td class="paramname">viterbi_mtbl</td><td>Name of table to store the m factors. </td></tr>
     <tr><td class="paramname">viterbi_rtbl</td><td>Name of table to store the r factors. </td></tr>
   </table>
   </dd>
 </dl>

 </div>
 </div>
 <a id="a3548bd640c2af456f3755cb24b913ebd"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a3548bd640c2af456f3755cb24b913ebd">&#9670;&nbsp;</a></span>crf_train_fgen()</h2>

 <div class="memitem">
 <div class="memproto">
       <table class="memname">
         <tr>
           <td class="memname">void crf_train_fgen </td>
           <td>(</td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>train_segment_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>regex_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>label_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>dictionary_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>train_feature_tbl</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">text&#160;</td>
           <td class="paramname"><em>train_featureset_tbl</em>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
         </tr>
       </table>
 </div><div class="memdoc">
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramname">train_segment_tbl</td><td>Name of table containing all the tokenized training sentences. </td></tr>
     <tr><td class="paramname">regex_tbl</td><td>Name of table containing all the regular expressions to capture regex features. </td></tr>
     <tr><td class="paramname">label_tbl</td><td>Name of the label table containing unique ids and label names. </td></tr>
     <tr><td class="paramname">dictionary_tbl</td><td>Name of table containing the dictionary_tbl. </td></tr>
     <tr><td class="paramname">train_feature_tbl</td><td>features generated from the traning dataset </td></tr>
     <tr><td class="paramname">train_featureset_tbl</td><td>unique feature set generated from the training dataset </td></tr>
   </table>
   </dd>
 </dl>

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	<div class="header">
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	<a href="#func-members">Functions</a> </div>
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	<div class="title">crf_feature_gen.sql_in File Reference</div> </div>
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	<p>SQL function for POS/NER feature extraction.
	<a href="#details">More...</a></p>
	<table class="memberdecls">
	<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
	Functions</h2></td></tr>
	<tr class="memitem:a3548bd640c2af456f3755cb24b913ebd"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html#a3548bd640c2af456f3755cb24b913ebd">crf_train_fgen</a> (text train_segment_tbl, text regex_tbl, text label_tbl, text dictionary_tbl, text train_feature_tbl, text train_featureset_tbl)</td></tr>
	<tr class="memdesc:a3548bd640c2af456f3755cb24b913ebd"><td class="mdescLeft"> </td><td class="mdescRight">This function extracts POS/NER features from the training data. <a href="#a3548bd640c2af456f3755cb24b913ebd">More...</a><br /></td></tr>
	<tr class="separator:a3548bd640c2af456f3755cb24b913ebd"><td class="memSeparator" colspan="2"> </td></tr>
	<tr class="memitem:ad9af2c3d1a4f1c937795634ca297dede"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="crf__feature__gen_8sql__in.html#ad9af2c3d1a4f1c937795634ca297dede">crf_test_fgen</a> (text test_segment_tbl, text dictionary_tbl, text label_tbl, text regex_tbl, text crf_weights_tbl, text viterbi_mtbl, text viterbi_rtbl)</td></tr>
	<tr class="memdesc:ad9af2c3d1a4f1c937795634ca297dede"><td class="mdescLeft"> </td><td class="mdescRight">This function extracts POS/NER features from the testing data. <a href="#ad9af2c3d1a4f1c937795634ca297dede">More...</a><br /></td></tr>
	<tr class="separator:ad9af2c3d1a4f1c937795634ca297dede"><td class="memSeparator" colspan="2"> </td></tr>
	</table>
	<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
	<div class="textblock"><dl class="section date"><dt>Date</dt><dd>February 2012</dd></dl>
	<dl class="section see"><dt>See also</dt><dd>For an introduction to POS/NER feature extraction, see the module description <a class="el" href="group__grp__crf.html">Conditional Random Field</a> </dd></dl>
	</div><h2 class="groupheader">Function Documentation</h2>
	<a id="ad9af2c3d1a4f1c937795634ca297dede"></a>
	<h2 class="memtitle"><span class="permalink"><a href="#ad9af2c3d1a4f1c937795634ca297dede">◆ </a></span>crf_test_fgen()</h2>

	<div class="memitem">
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	<td class="memname">void crf_test_fgen </td>
	<td>(</td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>test_segment_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>dictionary_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>label_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>regex_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>crf_weights_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>viterbi_mtbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>viterbi_rtbl</em> </td>
	</tr>
	<tr>
	<td></td>
	<td>)</td>
	<td></td><td></td>
	</tr>
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	</div><div class="memdoc">
	<p>This feature extraction function will produce two factor tables, "m table" (<em>viterbi_mtbl</em>) and "r table" (<em>viterbi_rtbl</em>). The <em>viterbi_mtbl</em> table and <em>viterbi_rtbl</em> table are used to calculate the best label sequence for each sentence.</p>
	<ul>
	<li><em>viterbi_mtbl</em> table encodes the edge features which are solely dependent on upon current label and previous y value. The m table has three columns which are prev_label, label, and value respectively. If the number of labels in \( n \), then the m factor table will \( n^2 \) rows. Each row encodes the transition feature weight value from the previous label to the current label.</li>
	</ul>
	<p><em>startFeature</em> is considered as a special edge feature which is from the beginning to the first token. Likewise, <em>endFeature</em> can be considered as a special edge feature which is from the last token to the very end. So m table encodes the edgeFeature, startFeature, and endFeature. If the total number of labels in the label space is 45 from 0 to 44, then the m factor array is as follows: </p><pre>
	0 1 2 3 4 5...44
	startFeature -1 a a a a a a...a
	edgeFeature 0 a a a a a a...a
	edgeFeature 1 a a a a a a...a
	...
	edgeFeature 44 a a a a a a...a
	endFeature 45 a a a a a a...a</pre><ul>
	<li>viterbi_r table is related to specific tokens. It encodes the single state features, e.g., wordFeature, RegexFeature for all tokens. The r table is represented in the following way. <pre>
	0 1 2 3 4...44
	token1 a a a a a...a
	token2 a a a a a...a</pre></li>
	</ul>
	<dl class="params"><dt>Parameters</dt><dd>
	<table class="params">
	<tr><td class="paramname">test_segment_tbl</td><td>Name of table containing all the tokenized testing sentences. </td></tr>
	<tr><td class="paramname">dictionary_tbl</td><td>Name of table containing the dictionary_tbl. </td></tr>
	<tr><td class="paramname">label_tbl</td><td>Name of table containing the the label space used in POS or other NLP tasks. </td></tr>
	<tr><td class="paramname">regex_tbl</td><td>Name of table containing all the regular expressions to capture regex features. </td></tr>
	<tr><td class="paramname">crf_weights_tbl</td><td>Name of the table containing featureset weights. </td></tr>
	<tr><td class="paramname">viterbi_mtbl</td><td>Name of table to store the m factors. </td></tr>
	<tr><td class="paramname">viterbi_rtbl</td><td>Name of table to store the r factors. </td></tr>
	</table>
	</dd>
	</dl>

	</div>
	</div>
	<a id="a3548bd640c2af456f3755cb24b913ebd"></a>
	<h2 class="memtitle"><span class="permalink"><a href="#a3548bd640c2af456f3755cb24b913ebd">◆ </a></span>crf_train_fgen()</h2>

	<div class="memitem">
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	<table class="memname">
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	<td class="memname">void crf_train_fgen </td>
	<td>(</td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>train_segment_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>regex_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>label_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>dictionary_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>train_feature_tbl</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">text </td>
	<td class="paramname"><em>train_featureset_tbl</em> </td>
	</tr>
	<tr>
	<td></td>
	<td>)</td>
	<td></td><td></td>
	</tr>
	</table>
	</div><div class="memdoc">
	<dl class="params"><dt>Parameters</dt><dd>
	<table class="params">
	<tr><td class="paramname">train_segment_tbl</td><td>Name of table containing all the tokenized training sentences. </td></tr>
	<tr><td class="paramname">regex_tbl</td><td>Name of table containing all the regular expressions to capture regex features. </td></tr>
	<tr><td class="paramname">label_tbl</td><td>Name of the label table containing unique ids and label names. </td></tr>
	<tr><td class="paramname">dictionary_tbl</td><td>Name of table containing the dictionary_tbl. </td></tr>
	<tr><td class="paramname">train_feature_tbl</td><td>features generated from the traning dataset </td></tr>
	<tr><td class="paramname">train_featureset_tbl</td><td>unique feature set generated from the training dataset </td></tr>
	</table>
	</dd>
	</dl>

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