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Beim Ausführen von Syntaxnet gibt es eine Menge Ausgaben auf der Konsole. Ich habe mich gefragt, wie ich die Abhängigkeitsdaten nur herausbekommen kann. So wie es jetzt ist dies meine Ausgabe lautet:Wie man nur den Syntaxbaum ausgibt
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 _ _
Was ich tun möchte, ist das Python-Skript aufrufen, ohne all diese Ausgabe an die Konsole, und nur die CONLL Daten.