如何输出解析树

Question

当运行syntaxnet时，有很多输出到控制台。我想知道如何才能获得依赖数据。因为它是现在这是我的输出：

I syntaxnet/term_frequency_map.cc:101] Loaded 37 terms from work/models/label-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 37 terms from work/models/label-map. 
I syntaxnet/embedding_feature_extractor.cc:35] Features: stack(3).word stack(2).word stack(1).word stack.word input.word input(1).word input(2).word input(3).word;input.digit input.hyphen;stack.suffix(length=2) input.suffix(length=2) input(1).suffix(length=2);stack.prefix(length=2) input.prefix(length=2) input(1).prefix(length=2) 
I syntaxnet/embedding_feature_extractor.cc:36] Embedding names: words;other;suffix;prefix 
I syntaxnet/embedding_feature_extractor.cc:37] Embedding dims: 64;4;8;8 
I syntaxnet/embedding_feature_extractor.cc:35] Features: input.word input(1).word input(2).word input(3).word stack.word stack(1).word stack(2).word stack(3).word stack.child(1).word stack.child(1).sibling(-1).word stack.child(-1).word stack.child(-1).sibling(1).word stack(1).child(1).word stack(1).child(1).sibling(-1).word stack(1).child(-1).word stack(1).child(-1).sibling(1).word stack.child(2).word stack.child(-2).word stack(1).child(2).word stack(1).child(-2).word;input.tag input(1).tag input(2).tag input(3).tag stack.tag stack(1).tag stack(2).tag stack(3).tag stack.child(1).tag stack.child(1).sibling(-1).tag stack.child(-1).tag stack.child(-1).sibling(1).tag stack(1).child(1).tag stack(1).child(1).sibling(-1).tag stack(1).child(-1).tag stack(1).child(-1).sibling(1).tag stack.child(2).tag stack.child(-2).tag stack(1).child(2).tag stack(1).child(-2).tag;stack.child(1).label stack.child(1).sibling(-1).label stack.child(-1).label stack.child(-1).sibling(1).label stack(1).child(1).label stack(1).child(1).sibling(-1).label stack(1).child(-1).label stack(1).child(-1).sibling(1).label stack.child(2).label stack.child(-2).label stack(1).child(2).label stack(1).child(-2).label 
I syntaxnet/embedding_feature_extractor.cc:36] Embedding names: words;tags;labels 
I syntaxnet/embedding_feature_extractor.cc:37] Embedding dims: 64;32;32 
I syntaxnet/term_frequency_map.cc:101] Loaded 29448 terms from work/models/word-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 29448 terms from work/models/word-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 17 terms from work/models/tag-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 17 terms from work/models/tag-map. 
INFO:tensorflow:Building training network with parameters: feature_sizes: [20 20 12] domain_sizes: [29451 20 40] 
INFO:tensorflow:Building training network with parameters: feature_sizes: [8 2 3 3] domain_sizes: [29451  5 3539 5064] 
I syntaxnet/embedding_feature_extractor.cc:35] Features: stack(3).word stack(2).word stack(1).word stack.word input.word input(1).word input(2).word input(3).word;input.digit input.hyphen;stack.suffix(length=2) input.suffix(length=2) input(1).suffix(length=2);stack.prefix(length=2) input.prefix(length=2) input(1).prefix(length=2) 
I syntaxnet/embedding_feature_extractor.cc:36] Embedding names: words;other;suffix;prefix 
I syntaxnet/embedding_feature_extractor.cc:37] Embedding dims: 64;4;8;8 
I syntaxnet/term_frequency_map.cc:101] Loaded 29448 terms from work/models/word-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 17 terms from work/models/tag-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 37 terms from work/models/label-map. 
I syntaxnet/reader_ops.cc:141] Starting epoch 1 
I syntaxnet/reader_ops.cc:141] Starting epoch 2 
INFO:tensorflow:Processed 1 documents 
INFO:tensorflow:Total processed documents: 1 
INFO:tensorflow:num correct tokens: 0 
INFO:tensorflow:total tokens: 5 
INFO:tensorflow:Seconds elapsed in evaluation: 0.05, eval metric: 0.00% 
I syntaxnet/term_frequency_map.cc:101] Loaded 37 terms from work/models/label-map. 
I syntaxnet/embedding_feature_extractor.cc:35] Features: input.word input(1).word input(2).word input(3).word stack.word stack(1).word stack(2).word stack(3).word stack.child(1).word stack.child(1).sibling(-1).word stack.child(-1).word stack.child(-1).sibling(1).word stack(1).child(1).word stack(1).child(1).sibling(-1).word stack(1).child(-1).word stack(1).child(-1).sibling(1).word stack.child(2).word stack.child(-2).word stack(1).child(2).word stack(1).child(-2).word;input.tag input(1).tag input(2).tag input(3).tag stack.tag stack(1).tag stack(2).tag stack(3).tag stack.child(1).tag stack.child(1).sibling(-1).tag stack.child(-1).tag stack.child(-1).sibling(1).tag stack(1).child(1).tag stack(1).child(1).sibling(-1).tag stack(1).child(-1).tag stack(1).child(-1).sibling(1).tag stack.child(2).tag stack.child(-2).tag stack(1).child(2).tag stack(1).child(-2).tag;stack.child(1).label stack.child(1).sibling(-1).label stack.child(-1).label stack.child(-1).sibling(1).label stack(1).child(1).label stack(1).child(1).sibling(-1).label stack(1).child(-1).label stack(1).child(-1).sibling(1).label stack.child(2).label stack.child(-2).label stack(1).child(2).label stack(1).child(-2).label 
I syntaxnet/embedding_feature_extractor.cc:36] Embedding names: words;tags;labels 
I syntaxnet/embedding_feature_extractor.cc:37] Embedding dims: 64;32;32 
I syntaxnet/term_frequency_map.cc:101] Loaded 29448 terms from work/models/word-map. 
I syntaxnet/term_frequency_map.cc:101] Loaded 17 terms from work/models/tag-map. 
INFO:tensorflow:Processed 1 documents 
INFO:tensorflow:Total processed documents: 1 
INFO:tensorflow:num correct tokens: 1 
INFO:tensorflow:total tokens: 5 
INFO:tensorflow:Seconds elapsed in evaluation: 0.05, eval metric: 20.00% 
1  Jeg  _  PRON PRON _  3  nsubj _  _ 
2  vil  _  AUX  AUX  _  3  aux  _  _ 
3  bestille  _  VERB VERB _  0  ROOT _  _ 
4  en  _  DET  DET  _  5  det  _  _ 
5  flybillett  _  ADJ  ADJ  _  3  dobj _  _ 

我想要做的就是调用Python脚本没有这一切输出到控制台，只是CONLL数据。

Answer 1

您可以将标准错误只是重定向到/ dev/null的：

root@67e2e1378a9b:~/models/syntaxnet# echo "I'm testing." | syntaxnet/demo.sh 2> /dev/null 
Input: I 'm testing . 
Parse: 
testing VBG ROOT 
+-- I PRP nsubj 
+-- 'm VBP aux 
+-- . . punct