依存グラフをPythonで保存する

Question

私は依存関係グラフを返す文を解析するためにpython3スタンフォード依存関係パーサーを使用しています。

import pickle 
from nltk.parse.stanford import StanfordDependencyParser 

parser = StanfordDependencyParser('stanford-parser-full-2015-12-09/stanford-parser.jar', 'stanford-parser-full-2015-12-09/stanford-parser-3.6.0-models.jar') 
sentences = ["I am going there","I am asking a question"] 
with open("save.p","wb") as f: 
     pickle.dump(parser.raw_parse_sents(sentences),f) 

それはエラーを与える：

AttributeError: Can't pickle local object 'DependencyGraph.__init__.<locals>.<lambda>' 

私はとか、漬物もしなくても依存関係グラフを保存することができれば、私は疑問に思います。

Answer 1

instructions to get a parsed outputに続きます。

1. CONLLフォーマットへの出力DependencyGraphと

をファイルに書き込め（What is CoNLL data format?とWhat does the dependency-parse output of TurboParser mean?を参照してください）

$ export STANFORDTOOLSDIR=$HOME 
$ export CLASSPATH=$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser.jar:$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser-3.6.0-models.jar 
$ python 
>>> from nltk.parse.stanford import StanfordDependencyParser 
>>> dep_parser=StanfordDependencyParser(model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz") 
>>> sent = "The quick brown fox jumps over the lazy dog." 
>>> output = next(dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")) 
>>> type(output) 
<class 'nltk.parse.dependencygraph.DependencyGraph'> 
>>> output.to_conll(style=4) # The *style* parameter just means that we want 4 columns in the CONLL format 
u'The\tDT\t4\tdet\nquick\tJJ\t4\tamod\nbrown\tJJ\t4\tamod\nfox\tNN\t5\tnsubj\njumps\tVBZ\t0\troot\nover\tIN\t9\tcase\nthe\tDT\t9\tdet\nlazy\tJJ\t9\tamod\ndog\tNN\t5\tnmod\n' 
>>> with open('sent.conll', 'w') as fout: 
...  fout.write(output.to_conll(4)) 
... 
>>> exit() 
$ cat sent.conll 
The DT 4 det 
quick JJ 4 amod 
brown JJ 4 amod 
fox NN 5 nsubj 
jumps VBZ 0 root 
over IN 9 case 
the DT 9 det 
lazy JJ 9 amod 
dog NN 5 nmod 

2 NLTK

にDependencyGraphに

>>> from nltk.parse.dependencygraph import DependencyGraph 
>>> output = DependencyGraph.load('sent.conll') # Loads any CONLL file, that might contain 1 or more sentences. 
>>> output # list of DependencyGraphs 
[<DependencyGraph with 10 nodes>] 
>>> output[0] # the first DependencyGraph, the one we have saved 
<DependencyGraph with 10 nodes> 
>>> print output[0] 
defaultdict(<function <lambda> at 0x10e83c758>, {0: {u'ctag': u'TOP', u'head': None, u'word': None, u'deps': defaultdict(<type 'list'>, {u'ROOT': [], u'root': [5]}), u'lemma': None, u'tag': u'TOP', u'rel': None, u'address': 0, u'feats': None}, 1: {u'ctag': u'DT', u'head': 4, u'deps': defaultdict(<type 'list'>, {}), u'tag': u'DT', u'address': 1, u'word': u'The', u'lemma': u'The', u'rel': u'det', u'feats': u''}, 2: {u'ctag': u'JJ', u'head': 4, u'deps': defaultdict(<type 'list'>, {}), u'tag': u'JJ', u'address': 2, u'word': u'quick', u'lemma': u'quick', u'rel': u'amod', u'feats': u''}, 3: {u'ctag': u'JJ', u'head': 4, u'deps': defaultdict(<type 'list'>, {}), u'tag': u'JJ', u'address': 3, u'word': u'brown', u'lemma': u'brown', u'rel': u'amod', u'feats': u''}, 4: {u'ctag': u'NN', u'head': 5, u'deps': defaultdict(<type 'list'>, {u'det': [1], u'amod': [2, 3]}), u'tag': u'NN', u'address': 4, u'word': u'fox', u'lemma': u'fox', u'rel': u'nsubj', u'feats': u''}, 5: {u'ctag': u'VBZ', u'head': 0, u'deps': defaultdict(<type 'list'>, {u'nmod': [9], u'nsubj': [4]}), u'tag': u'VBZ', u'address': 5, u'word': u'jumps', u'lemma': u'jumps', u'rel': u'root', u'feats': u''}, 6: {u'ctag': u'IN', u'head': 9, u'deps': defaultdict(<type 'list'>, {}), u'tag': u'IN', u'address': 6, u'word': u'over', u'lemma': u'over', u'rel': u'case', u'feats': u''}, 7: {u'ctag': u'DT', u'head': 9, u'deps': defaultdict(<type 'list'>, {}), u'tag': u'DT', u'address': 7, u'word': u'the', u'lemma': u'the', u'rel': u'det', u'feats': u''}, 8: {u'ctag': u'JJ', u'head': 9, u'deps': defaultdict(<type 'list'>, {}), u'tag': u'JJ', u'address': 8, u'word': u'lazy', u'lemma': u'lazy', u'rel': u'amod', u'feats': u''}, 9: {u'ctag': u'NN', u'head': 5, u'deps': defaultdict(<type 'list'>, {u'case': [6], u'det': [7], u'amod': [8]}), u'tag': u'NN', u'address': 9, u'word': u'dog', u'lemma': u'dog', u'rel': u'nmod', u'feats': u''}}) 

---

をCONLLファイルを読みます

の出力はnltk.tree.Treeではありません。DependencyGraphではありません（これはまったく同じです。

nltk.tree.Treeについては

$ export STANFORDTOOLSDIR=$HOME 
$ export CLASSPATH=$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser.jar:$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser-3.6.0-models.jar 
$ python 
>>> from nltk.parse.stanford import StanfordParser 
>>> parser=StanfordParser(model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz") 
>>> list(parser.raw_parse("the quick brown fox jumps over the lazy dog")) 
[Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']), Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']), Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])])] 
>>> output = list(parser.raw_parse("the quick brown fox jumps over the lazy dog")) 
>>> type(output[0]) 
<class 'nltk.tree.Tree'> 

あなたは括弧解析文字列として出力をすることができますし、ツリーオブジェクトに文字列を読み込む：

[conll]（http://www.nltk.org/apiにアウト

>>> from nltk import Tree 
>>> output[0] 
Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']), Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']), Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])]) 
>>> str(output[0]) 
'(ROOT\n (NP\n (NP (DT the) (JJ quick) (JJ brown) (NN fox))\n (NP\n  (NP (NNS jumps))\n  (PP (IN over) (NP (DT the) (JJ lazy) (NN dog))))))' 
>>> parsed_sent = str(output[0]) 
>>> type(parsed_sent) 
<type 'str'> 
>>> Tree.fromstring(parsed_sent) 
Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']), Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']), Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])]) 
>>> parsed_tree = Tree.fromstring(parsed_sent) 
>>> type(parsed_tree) 
<class 'nltk.tree.Tree'>