Add preordering agreement feature.

Author: sebschu

CONTRIBUTING.md

@@ -13,4 +13,4 @@ Therefore, we can accept contributions on any of the following terms:
  * You can make your contribution available under a non-restrictive open source license, such as the Revised (or 3-clause) BSD license, with appropriate licensing information included with the submitted code.
  * You can sign and return to us a contributor license agreement (CLA), explicitly licensing us to be able to use the code. You can find these agreements at http://nlp.stanford.edu/software/CLA/ . You can send them to us or contact us at: [email protected] .
 
-You should do development against our master branch. You should make sure that all unit tests still pass. (In general, you will not be able to run our integration tests, since they rely on resources in our filesystem.)
+You should do development against our master branch. The project's source code is in utf-8 character encoding. You should make sure that all unit tests still pass. (In general, you will not be able to run our integration tests, since they rely on resources in our filesystem.)

data/edu/stanford/nlp/classify/iris.gold

@@ -1,20 +1,20 @@
-5	Iris-setosa	Iris-setosa	0.995615365125735
-4.6	Iris-setosa	Iris-setosa	0.9994804135630505
-5.1	Iris-setosa	Iris-setosa	0.9937095680980086
-4.9	Iris-setosa	Iris-setosa	0.9905109629700247
-5.4	Iris-setosa	Iris-setosa	0.9982151488134486
-4.4	Iris-setosa	Iris-setosa	0.9944214428148407
-5.3	Iris-setosa	Iris-setosa	0.9984497925740373
-6.1	Iris-versicolor	Iris-versicolor	0.8873152482428373
-6	Iris-versicolor	Iris-versicolor	0.9424246013278404
-5.5	Iris-versicolor	Iris-versicolor	0.9030026595536319
-6.5	Iris-versicolor	Iris-versicolor	0.928816167001929
-6.8	Iris-versicolor	Iris-versicolor	0.9569376555329442
-6.2	Iris-versicolor	Iris-versicolor	0.9857141927233324
-6.7	Iris-virginica	Iris-virginica	0.9698639532763317
-6.4	Iris-virginica	Iris-virginica	0.8982390073296296
-5.7	Iris-virginica	Iris-virginica	0.9920401400173403
-6.7	Iris-virginica	Iris-virginica	0.968576539063806
-6.8	Iris-virginica	Iris-virginica	0.9957320369272686
-7.7	Iris-virginica	Iris-virginica	0.9900526044768513
-7.3	Iris-virginica	Iris-virginica	0.9766204287594443
+5	Iris-setosa	Iris-setosa	0.996	0.996
+4.6	Iris-setosa	Iris-setosa	0.999	0.999
+5.1	Iris-setosa	Iris-setosa	0.994	0.994
+4.9	Iris-setosa	Iris-setosa	0.991	0.991
+5.4	Iris-setosa	Iris-setosa	0.998	0.998
+4.4	Iris-setosa	Iris-setosa	0.994	0.994
+5.3	Iris-setosa	Iris-setosa	0.998	0.998
+6.1	Iris-versicolor	Iris-versicolor	0.887	0.887
+6	Iris-versicolor	Iris-versicolor	0.942	0.942
+5.5	Iris-versicolor	Iris-versicolor	0.903	0.903
+6.5	Iris-versicolor	Iris-versicolor	0.929	0.929
+6.8	Iris-versicolor	Iris-versicolor	0.957	0.957
+6.2	Iris-versicolor	Iris-versicolor	0.986	0.986
+6.7	Iris-virginica	Iris-virginica	0.970	0.970
+6.4	Iris-virginica	Iris-virginica	0.898	0.898
+5.7	Iris-virginica	Iris-virginica	0.992	0.992
+6.7	Iris-virginica	Iris-virginica	0.969	0.969
+6.8	Iris-virginica	Iris-virginica	0.996	0.996
+7.7	Iris-virginica	Iris-virginica	0.990	0.990
+7.3	Iris-virginica	Iris-virginica	0.977	0.977

data/edu/stanford/nlp/classify/iris.l1reg.gold

@@ -1,20 +1,20 @@
-5       Iris-setosa     Iris-setosa     0.9919247137755053
-4.6     Iris-setosa     Iris-setosa     0.9988153870786971
-5.1     Iris-setosa     Iris-setosa     0.9893228231715544
-4.9     Iris-setosa     Iris-setosa     0.9835318845429561
-5.4     Iris-setosa     Iris-setosa     0.9960427411240634
-4.4     Iris-setosa     Iris-setosa     0.9910859075339642
-5.3     Iris-setosa     Iris-setosa     0.9965862883009643
-6.1     Iris-versicolor Iris-versicolor 0.8468902641192759
-6       Iris-versicolor Iris-versicolor 0.9307517829994151
-5.5     Iris-versicolor Iris-versicolor 0.7982164305911292
-6.5     Iris-versicolor Iris-versicolor 0.873020490772672
-6.8     Iris-versicolor Iris-versicolor 0.9142958840729118
-6.2     Iris-versicolor Iris-versicolor 0.9691329948474605
-6.7     Iris-virginica  Iris-virginica  0.9514065325627161
-6.4     Iris-virginica  Iris-virginica  0.8326970803989662
-5.7     Iris-virginica  Iris-virginica  0.9861478471561218
-6.7     Iris-virginica  Iris-virginica  0.9281387678310443
-6.8     Iris-virginica  Iris-virginica  0.9869791941203433
-7.7     Iris-virginica  Iris-virginica  0.980694494307154
-7.3     Iris-virginica  Iris-virginica  0.9555631398239129
+5       Iris-setosa     Iris-setosa     0.992	0.992
+4.6     Iris-setosa     Iris-setosa     0.999	0.999
+5.1     Iris-setosa     Iris-setosa     0.989	0.989
+4.9     Iris-setosa     Iris-setosa     0.984	0.984
+5.4     Iris-setosa     Iris-setosa     0.996	0.996
+4.4     Iris-setosa     Iris-setosa     0.991	0.991
+5.3     Iris-setosa     Iris-setosa     0.997	0.997
+6.1     Iris-versicolor Iris-versicolor 0.847	0.847
+6       Iris-versicolor Iris-versicolor 0.931	0.931
+5.5     Iris-versicolor Iris-versicolor 0.798	0.798
+6.5     Iris-versicolor Iris-versicolor 0.873	0.873
+6.8     Iris-versicolor Iris-versicolor 0.914	0.914
+6.2     Iris-versicolor Iris-versicolor 0.969	0.969
+6.7     Iris-virginica  Iris-virginica  0.951	0.951
+6.4     Iris-virginica  Iris-virginica  0.833	0.833
+5.7     Iris-virginica  Iris-virginica  0.986	0.986
+6.7     Iris-virginica  Iris-virginica  0.928	0.928
+6.8     Iris-virginica  Iris-virginica  0.987	0.987
+7.7     Iris-virginica  Iris-virginica  0.981	0.981
+7.3     Iris-virginica  Iris-virginica  0.956	0.956

test/src/edu/stanford/nlp/neural/EmbeddingTest.java itest/src/edu/stanford/nlp/neural/EmbeddingITest.java

@@ -13,7 +13,7 @@
  *
  */
 
-public class EmbeddingTest {
+public class EmbeddingITest {
   public static final String wordVectorFile = "edu/stanford/nlp/neural/wordVector.txt";
   public static final String wordFile = "edu/stanford/nlp/neural/word.txt";
   public static final String vectorFile = "edu/stanford/nlp/neural/vector.txt";

scripts/makeSerialized.csh

@@ -145,7 +145,7 @@ java -mx1500m edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA
 # This now works
 ( echo "Running xinhuaFactored from serialized (check without specifying -tLPP) on $host -server" ; time java -server -mx1800m edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA,tsv" -maxLength 40 -loadFromSerializedFile xinhuaFactored.ser.gz -test $ctb 001-025 ) >>& ./serializedParsers.log
 
-( echo "Running chinesePCFG (simplified for use in the RNN parser) on $host -server" ; time java -server -mx4g edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA,tsv" -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams -chineseFactored -PCFG -compactGrammar 0 -saveToSerializedFile chinesePCFG-simple.ser.gz -maxLength 40 -train $ctb7train -test $ctb7test ) >>& ./serializedParsers.log
+( echo "Running chinesePCFG (simplified for use in the RNN parser) on $host -server" ; time java -server -mx4g edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA,tsv" -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams -chineseFactored -PCFG -hMarkov 1 -nomarkNPconj -compactGrammar 0 -saveToSerializedFile chinesePCFG.simple.ser.gz -maxLength 40 -train $ctb7train -test $ctb7test ) >>& ./serializedParsers.log
 
 # German Factored binary from Negra (version 2)
 # $negra 3 is the dev set