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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 class="anchor" id="ad9af2c3d1a4f1c937795634ca297dede"></a> |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <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> |
| </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 class="anchor" id="a3548bd640c2af456f3755cb24b913ebd"></a> |
| <div class="memitem"> |
| <div class="memproto"> |
| <table class="memname"> |
| <tr> |
| <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> |
| |
| </div> |
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