获得从NLTK.tree结果实体

Question

我的原始句子是

地震海啸也被连接到一个薄的 层沿着 板接口的最上部俯冲沉积岩的存在，如被认为存在于海洋地壳顶部的显着 地形区域，以及向上倾向传播为 ，可能到达海底。

我通过了一句斯坦福NLP，并得到了很好的解析树：

（ROOT（S（NP（NN海啸）（NNS地震））（VP（VBP有）（ADVP（ （NP（DT））（NP存在））（VP（VBN存在））（VP（VBN存在））（NP（NP存在） ）（NP（NP）（JJ沉积）（NN岩石））（PP （IN沿）（NP（NP（NP） NP（DT）（JJS最上面）（NN部分））（PP（IN）） （NP（DT））（NN板）（NN界面））））（，，）（UCP（RB as）（VP（VBN想法）（S（VP（TO to）（VP（VB be）（ADJP））（PP（IN in）（NP（NP（NNS areas）） PP（IN（IN））（NP（IN））（NP（IN））（NP（IN））（NP（IN） DT）（JJ oceanic）（NN crust）））））））））））（，，） （CC and）（SBAR（WHADVP（WRB where））（S（NP（NN propagation）） （VBD was）（PP（INin）（NP（DT an）（JJ up-dip）（NN direction）））（，）（ADVP （可能的RB）））））））））（S （VP（VBG达到）（NP（DT the）（NN seafloor））））））））））））））（。 。）））

然后我喂以上字符串到NLTK。树：

pasrsd_tree = NLTK.Tree.fromstring(parsetree_string) 

结果是相当不错的：

树（ '根'，[树（ 'S'，[树（ 'NP'，[树（ 'NN'，[” （''''））， 树（'ADVP'，[树'（''''））， 树（'NNS'，['earthquakequakes']）] （''''））]），树（'VP'，[树（'VBN'， ['been']），树（'VP' ''）），树（'PP'，[Tree（'TO'， ['to']），树（'NP'，[Tree（'NP'，[Tree（'DT'，['the' ]），Tree（'NN'， ['presence']）]），树（'PP'，[Tree（'IN'，''of']），树（'NP'， [Tree（'NP'，[Tree（'DT'，['a']），Tree（'JJ'，['thin']），Tree（'NN'， ['layer']）]）） ，Tree（'PP'，[Tree（'IN'，''of']），Tree（'S'，[Tree（'VP'， [Tree（'VBN'，['subducted']），Tree （'NP'，[Tree（'NP'，[Tree（'JJ'， ['sedimentary']），Tree（'NN'，['rock']）]），Tree（'PP'，[Tree （'IN'， ['along']），Tree（'NP'，[Tree（'NP'，[Tree（'NP'，[Tree（'DT'，''''））， Tree （'''，['of']），树（''''），树（'NN'，['部分']）]），树（'PP'， ' NP'，[Tree（'DT'，['the']），Tree（'NN'， ['plate']），Tree（'NN'，['interface']）]）]）]），树（'，'，['，']）， 树（'UCP'，[树（'RB'，['as']），树（'S'，[树（'VP'，[Tree 'VB'， ['is']），树（'VP'，[Tree（'VBN'，['thought']），树（'S'，[树''VP'， [至'， ['to']），Tree（'VP'，[Tree（'VB'，['be']），Tree（'ADJP'， [Tree（'JJ'，['present']），Tree ''树'（'IN'，['in']），树（'NP'， [Tree（'NP'，[Tree（'NNS'，['areas']）]），Tree （''''），树（'NN'， ['topography'，''''），'树' （Tree'（'''，''at']），Tree（'NP'， [Tree（'NP'，[Tree（' ''''），树（'NN'，['top']）]），树（'PP'， [Tree（'IN'，['of']），树（' NP'，[Tree（'DT'，['the']），Tree（'JJ'， ['oceanic']），Tree（'NN'，['crust']）]）]）]）] ）]）]）]）]）]）]），树（'，'， ['，']），树（'CC'，['和']），树（'SBAR'，[Tree 'WHADVP'，[Tree（'WRB'， ['where']）]），Tree（'S'，[Tree（'NP'，[Tree（'NN'，['propagation']）]）， 树（'VP'，[Tree（'VBD'， （''was']），Tree（'PP'，[Tree（'IN'，['in']）， 树（'NP'，[Tree（'DT'，['an']），Tree 'JJ'，['up-dip']），树（'NN'， ['direction']）]）]），树（'，'，['，']），树（'ADVP'， Tree（'S'，[Tree（'VP'，[Tree（'VBG'）] [Tree''''''））Tree（'RB'， ['possible']）]）]）]）） （''''）），树（'NP'，[Tree（'DT'，['the']），Tree（'NN'， ['seafloor']）]）]）]）]]） ]））]）]）]）]））]）]）]），Tree（'。'，['。']）]）]）

我的问题是，如果是pared_tree，我可以得到像top of the oceanic crust，a thin layer这样的左边的实体吗？

我在考虑解析树的级别可能很有用，但是在查看树级别时我真的迷失了方向，不知道该怎么做。

我主要是基于Python的，斯坦福大学的NLP结果是使用Python包装器（https://bitbucket.org/torotoki/corenlp-python）获得的。

任何人都可以帮助我，也许指出一些方向？

Answer 1

你可以尝试提取被标记NP子树：

>>> from nltk import Tree 
>>> parsed_tree = Tree('ROOT', [Tree('S', [Tree('NP', [Tree('NN', ['Tsunami']), Tree('NNS', ['earthquakes'])]), Tree('VP', [Tree('VBP', ['have']), Tree('ADVP', [Tree('RB', ['also'])]), Tree('VP', [Tree('VBN', ['been']), Tree('VP', [Tree('VBN', ['linked']), Tree('PP', [Tree('TO', ['to']), Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('NN', ['presence'])]), Tree('PP', [Tree('IN', ['of']), Tree('NP', [Tree('NP', [Tree('DT', ['a']), Tree('JJ', ['thin']), Tree('NN', ['layer'])]), Tree('PP', [Tree('IN', ['of']), Tree('S', [Tree('VP', [Tree('VBN', ['subducted']), Tree('NP', [Tree('NP', [Tree('JJ', ['sedimentary']), Tree('NN', ['rock'])]), Tree('PP', [Tree('IN', ['along']), Tree('NP', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJS', ['uppermost']), Tree('NN', ['part'])]), Tree('PP', [Tree('IN', ['of']), Tree('NP', [Tree('DT', ['the']), Tree('NN', ['plate']), Tree('NN', ['interface'])])])]), Tree(',', [',']), Tree('UCP', [Tree('RB', ['as']), Tree('S', [Tree('VP', [Tree('VBZ', ['is']), Tree('VP', [Tree('VBN', ['thought']), Tree('S', [Tree('VP', [Tree('TO', ['to']), Tree('VP', [Tree('VB', ['be']), Tree('ADJP', [Tree('JJ', ['present']), Tree('PP', [Tree('IN', ['in']), Tree('NP', [Tree('NP', [Tree('NNS', ['areas'])]), Tree('PP', [Tree('IN', ['of']), Tree('NP', [Tree('JJ', ['significant']), Tree('NN', ['topography'])])])])])]), Tree('PP', [Tree('IN', ['at']), Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('NN', ['top'])]), Tree('PP', [Tree('IN', ['of']), Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['oceanic']), Tree('NN', ['crust'])])])])])])])])])])]), Tree(',', [',']), Tree('CC', ['and']), Tree('SBAR', [Tree('WHADVP', [Tree('WRB', ['where'])]), Tree('S', [Tree('NP', [Tree('NN', ['propagation'])]), Tree('VP', [Tree('VBD', ['was']), Tree('PP', [Tree('IN', ['in']), Tree('NP', [Tree('DT', ['an']), Tree('JJ', ['up-dip']), Tree('NN', ['direction'])])]), Tree(',', [',']), Tree('ADVP', [Tree('RB', ['possibly'])])])])])])])])]), Tree('S', [Tree('VP', [Tree('VBG', ['reaching']), Tree('NP', [Tree('DT', ['the']), Tree('NN', ['seafloor'])])])])])])])])])])])])])]), Tree('.', ['.'])])]) 

>>> np = [" ".join(i.leaves()) for i in parsed_tree.subtrees() if i.label() == 'NP'] 
>>> np 
['Tsunami earthquakes', 'the presence of a thin layer of subducted sedimentary rock along the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly reaching the seafloor', 'the presence', 'a thin layer of subducted sedimentary rock along the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly reaching the seafloor', 'a thin layer', 'sedimentary rock along the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly', 'sedimentary rock', 'the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly', 'the uppermost part of the plate interface', 'the uppermost part', 'the plate interface', 'areas of significant topography', 'areas', 'significant topography', 'the top of the oceanic crust', 'the top', 'the oceanic crust', 'propagation', 'an up-dip direction', 'the seafloor'] 

但是，这会导致大量的噪音，让我们说没有一个字是一个短语：

>>> np_mwe 
['Tsunami earthquakes', 'the presence of a thin layer of subducted sedimentary rock along the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly reaching the seafloor', 'the presence', 'a thin layer of subducted sedimentary rock along the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly reaching the seafloor', 'a thin layer', 'sedimentary rock along the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly', 'sedimentary rock', 'the uppermost part of the plate interface , as is thought to be present in areas of significant topography at the top of the oceanic crust , and where propagation was in an up-dip direction , possibly', 'the uppermost part of the plate interface', 'the uppermost part', 'the plate interface', 'areas of significant topography', 'significant topography', 'the top of the oceanic crust', 'the top', 'the oceanic crust', 'an up-dip direction', 'the seafloor'] 

还是相当嘈杂，让我们假设一个名词短语不应该包含逗号（不必是真实但有用的技巧）：

>>> np_mwe_nocomma = [j for j in [" ".join(i.leaves()) for i in parsed_tree.subtrees() if i.label() == 'NP'] if j.count(' ') > 0 and j.count(',') == 0] 
>>> np_mwe_nocomma 
['Tsunami earthquakes', 'the presence', 'a thin layer', 'sedimentary rock', 'the uppermost part of the plate interface', 'the uppermost part', 'the plate interface', 'areas of significant topography', 'significant topography', 'the top of the oceanic crust', 'the top', 'the oceanic crust', 'an up-dip direction', 'the seafloor'] 

现在，我们很容易看到子树子树，所以我们选择把更大的子树：

>> x = [] 
>>> for i in sorted(np_mwe_nocomma, key=len, reverse=True): 
...  for j in x: 
...    if i in j: 
...      continue 
...  print i 
...  x.append(i) 
... 
the uppermost part of the plate interface 
areas of significant topography 
the top of the oceanic crust 
significant topography 
Tsunami earthquakes 
the plate interface 
an up-dip direction 
the uppermost part 
the oceanic crust 
sedimentary rock 
the presence 
a thin layer 
the seafloor 

我不知道这是否给你你需要什么，但你的“实体”的定义需要更具体否则几乎任何由解析器标记的NP都可能是一个“实体”