评估斯坦福NLP情绪时获得不同的结果

Question

我下载了斯坦福NLP 3.5.2，并使用默认配置运行情绪分析（即，我没有更改任何内容，只是解压缩并运行）。

java -cp "*" edu.stanford.nlp.sentiment.Evaluate -model edu/stanford/nlp/models/sentiment/sentiment.ser.gz -treebank test.txt

EVALUATION SUMMARY
Tested 82600 labels
  66258 correct
  16342 incorrect
  0.802155 accuracy
Tested 2210 roots
  976 correct
  1234 incorrect
  0.441629 accuracy
Label confusion matrix
      Guess/Gold       0       1       2       3       4    Marg. (Guess)
               0     323     161      27       3       3     517
               1    1294    5498    2245     652     148    9837
               2     292    2993   51972    2868     282   58407
               3      99     602    2283    7247    2140   12371
               4       0       1      21     228    1218    1468
    Marg. (Gold)    2008    9255   56548   10998    3791

               0        prec=0.62476, recall=0.16086, spec=0.99759, f1=0.25584
               1        prec=0.55891, recall=0.59406, spec=0.94084, f1=0.57595
               2        prec=0.88982, recall=0.91908, spec=0.75299, f1=0.90421
               3        prec=0.58581, recall=0.65894, spec=0.92844, f1=0.62022
               4        prec=0.8297, recall=0.32129, spec=0.99683, f1=0.46321

Root label confusion matrix
      Guess/Gold       0       1       2       3       4    Marg. (Guess)
               0      44      39       9       0       0      92
               1     193     451     190     131      36    1001
               2      23      62      82      30       8     205
               3      19      81     101     299     255     755
               4       0       0       7      50     100     157
    Marg. (Gold)     279     633     389     510     399

               0        prec=0.47826, recall=0.15771, spec=0.97514, f1=0.2372
               1        prec=0.45055, recall=0.71248, spec=0.65124, f1=0.55202
               2        prec=0.4, recall=0.2108, spec=0.93245, f1=0.27609
               3        prec=0.39603, recall=0.58627, spec=0.73176, f1=0.47273
               4        prec=0.63694, recall=0.25063, spec=0.96853, f1=0.35971

Approximate Negative label accuracy: 0.646009
Approximate Positive label accuracy: 0.732504
Combined approximate label accuracy: 0.695110
Approximate Negative root label accuracy: 0.797149
Approximate Positive root label accuracy: 0.774477
Combined approximate root label accuracy: 0.785832

test.txt文件下载自http://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip（包含train.txt、dev.txt和test.txt）。下载链接从http://nlp.stanford.edu/sentiment/code.html

然而，在论文"Socher， R.，Perelygin，A.，Wu，J.Y.，Chiang，J.，Manning，C.D.，Ng，A.Y.and Potts，C.，2013,10.情感树库上语义组合性的递归深度模型.在自然语言处理经验方法会议论文集（EMNLP）（Vol.1631，p.1642）."中，情感分析工具是基于哪个，作者报告了分类5类时的准确率为0.807。

我获得的结果正常吗？

Answer 1

当我把它从盒子里拿出来时，我得到了同样的结果。如果他们为斯坦福CoreNLP制作的系统版本与论文中的版本略有不同，我不会感到惊讶。