问题描述
我正在尝试在句子中进行主题提取，以便我可以根据主题获得情绪.为此，我在 python2.7 中使用 nltk.以下面这句话为例:
I am trying to work on subject extraction in a sentence, so that I can get the sentiments in accordance with the subject. I am using nltk in python2.7 for this purpose. Take the following sentence as an example:
唐纳德特朗普是美国最差的总统，但希拉里比他好
他我们可以看到Donald Trump和Hillary是两个主体，与Donald Trump相关的情绪是负面的，但与Hillary 是正面的.到现在为止，我能够将这句话分成几块名词短语，我能够得到以下内容:
He we can see that Donald Trump and Hillary are the two subjects, and sentiments related to Donald Trump is negative but related to Hillary are positive. Till now, I am able to break this sentence into chunks of noun phrases, and I am able to get the following:
(S
(NP Donald/NNP Trump/NNP)
is/VBZ
(NP the/DT worst/JJS president/NN)
in/IN
(NP USA,/NNP)
but/CC
(NP Hillary/NNP)
is/VBZ
better/JJR
than/IN
(NP him/PRP))
现在，我如何从这些名词短语中找到主语?那么我如何将这两个主题的短语组合在一起?一旦我拥有分别针对两个主题的短语，我就可以分别对它们进行情感分析.
Now, how do I approach in finding the subjects from these noun phrases? Then how do I group the phrases meant for both the subjects together? Once I have the phrases meant for both the subjects separately, I can perform sentiment analysis on both of them separately.
编辑
我查看了@Krzysiek (spacy) 提到的库，它在句子中也给了我依赖树.
I looked into the library mentioned by @Krzysiek (spacy), and it gave me dependency trees as well in the sentences.
代码如下:
from spacy.en import English
parser = English()
example = u"Donald Trump is the worst president of USA, but Hillary is better than him"
parsedEx = parser(example)
# shown as: original token, dependency tag, head word, left dependents, right dependents
for token in parsedEx:
print(token.orth_, token.dep_, token.head.orth_, [t.orth_ for t in token.lefts], [t.orth_ for t in token.rights])
这里是依赖树:
(u'Donald', u'compound', u'Trump', [], [])
(u'Trump', u'nsubj', u'is', [u'Donald'], [])
(u'is', u'ROOT', u'is', [u'Trump'], [u'president', u',', u'but', u'is'])
(u'the', u'det', u'president', [], [])
(u'worst', u'amod', u'president', [], [])
(u'president', u'attr', u'is', [u'the', u'worst'], [u'of'])
(u'of', u'prep', u'president', [], [u'USA'])
(u'USA', u'pobj', u'of', [], [])
(u',', u'punct', u'is', [], [])
(u'but', u'cc', u'is', [], [])
(u'Hillary', u'nsubj', u'is', [], [])
(u'is', u'conj', u'is', [u'Hillary'], [u'better'])
(u'better', u'acomp', u'is', [], [u'than'])
(u'than', u'prep', u'better', [], [u'him'])
(u'him', u'pobj', u'than', [], [])
这可以深入了解句子的不同标记的依赖关系.这是该论文的链接，该论文描述了不同对之间的依赖关系.如何使用这棵树将不同主题的上下文词附加到它们上?
This gives in depth insights into the dependencies of the different tokens of the sentences. Here is the link to the paper which describes the dependencies between different pairs. How can I use this tree to attach the contextual words for different subjects to them?
推荐答案
我对 spacy 库的了解比较多，最后通过依赖管理找到了解决方案.感谢 this repo，我想出了如何包含形容词以及我的主观动词宾语(使其成为 SVAO)，以及在查询中取出复合主语.这是我的解决方案:
I was going through spacy library more, and I finally figured out the solution through dependency management. Thanks to this repo, I figured out how to include adjectives as well in my subjective verb object (making it SVAO's), as well as taking out compound subjects in the query. Here goes my solution:
from nltk.stem.wordnet import WordNetLemmatizer
from spacy.lang.en import English
SUBJECTS = ["nsubj", "nsubjpass", "csubj", "csubjpass", "agent", "expl"]
OBJECTS = ["dobj", "dative", "attr", "oprd"]
ADJECTIVES = ["acomp", "advcl", "advmod", "amod", "appos", "nn", "nmod", "ccomp", "complm",
"hmod", "infmod", "xcomp", "rcmod", "poss"," possessive"]
COMPOUNDS = ["compound"]
PREPOSITIONS = ["prep"]
def getSubsFromConjunctions(subs):
moreSubs = []
for sub in subs:
# rights is a generator
rights = list(sub.rights)
rightDeps = {tok.lower_ for tok in rights}
if "and" in rightDeps:
moreSubs.extend([tok for tok in rights if tok.dep_ in SUBJECTS or tok.pos_ == "NOUN"])
if len(moreSubs) > 0:
moreSubs.extend(getSubsFromConjunctions(moreSubs))
return moreSubs
def getObjsFromConjunctions(objs):
moreObjs = []
for obj in objs:
# rights is a generator
rights = list(obj.rights)
rightDeps = {tok.lower_ for tok in rights}
if "and" in rightDeps:
moreObjs.extend([tok for tok in rights if tok.dep_ in OBJECTS or tok.pos_ == "NOUN"])
if len(moreObjs) > 0:
moreObjs.extend(getObjsFromConjunctions(moreObjs))
return moreObjs
def getVerbsFromConjunctions(verbs):
moreVerbs = []
for verb in verbs:
rightDeps = {tok.lower_ for tok in verb.rights}
if "and" in rightDeps:
moreVerbs.extend([tok for tok in verb.rights if tok.pos_ == "VERB"])
if len(moreVerbs) > 0:
moreVerbs.extend(getVerbsFromConjunctions(moreVerbs))
return moreVerbs
def findSubs(tok):
head = tok.head
while head.pos_ != "VERB" and head.pos_ != "NOUN" and head.head != head:
head = head.head
if head.pos_ == "VERB":
subs = [tok for tok in head.lefts if tok.dep_ == "SUB"]
if len(subs) > 0:
verbNegated = isNegated(head)
subs.extend(getSubsFromConjunctions(subs))
return subs, verbNegated
elif head.head != head:
return findSubs(head)
elif head.pos_ == "NOUN":
return [head], isNegated(tok)
return [], False
def isNegated(tok):
negations = {"no", "not", "n't", "never", "none"}
for dep in list(tok.lefts) + list(tok.rights):
if dep.lower_ in negations:
return True
return False
def findSVs(tokens):
svs = []
verbs = [tok for tok in tokens if tok.pos_ == "VERB"]
for v in verbs:
subs, verbNegated = getAllSubs(v)
if len(subs) > 0:
for sub in subs:
svs.append((sub.orth_, "!" + v.orth_ if verbNegated else v.orth_))
return svs
def getObjsFromPrepositions(deps):
objs = []
for dep in deps:
if dep.pos_ == "ADP" and dep.dep_ == "prep":
objs.extend([tok for tok in dep.rights if tok.dep_ in OBJECTS or (tok.pos_ == "PRON" and tok.lower_ == "me")])
return objs
def getAdjectives(toks):
toks_with_adjectives = []
for tok in toks:
adjs = [left for left in tok.lefts if left.dep_ in ADJECTIVES]
adjs.append(tok)
adjs.extend([right for right in tok.rights if tok.dep_ in ADJECTIVES])
tok_with_adj = " ".join([adj.lower_ for adj in adjs])
toks_with_adjectives.extend(adjs)
return toks_with_adjectives
def getObjsFromAttrs(deps):
for dep in deps:
if dep.pos_ == "NOUN" and dep.dep_ == "attr":
verbs = [tok for tok in dep.rights if tok.pos_ == "VERB"]
if len(verbs) > 0:
for v in verbs:
rights = list(v.rights)
objs = [tok for tok in rights if tok.dep_ in OBJECTS]
objs.extend(getObjsFromPrepositions(rights))
if len(objs) > 0:
return v, objs
return None, None
def getObjFromXComp(deps):
for dep in deps:
if dep.pos_ == "VERB" and dep.dep_ == "xcomp":
v = dep
rights = list(v.rights)
objs = [tok for tok in rights if tok.dep_ in OBJECTS]
objs.extend(getObjsFromPrepositions(rights))
if len(objs) > 0:
return v, objs
return None, None
def getAllSubs(v):
verbNegated = isNegated(v)
subs = [tok for tok in v.lefts if tok.dep_ in SUBJECTS and tok.pos_ != "DET"]
if len(subs) > 0:
subs.extend(getSubsFromConjunctions(subs))
else:
foundSubs, verbNegated = findSubs(v)
subs.extend(foundSubs)
return subs, verbNegated
def getAllObjs(v):
# rights is a generator
rights = list(v.rights)
objs = [tok for tok in rights if tok.dep_ in OBJECTS]
objs.extend(getObjsFromPrepositions(rights))
potentialNewVerb, potentialNewObjs = getObjFromXComp(rights)
if potentialNewVerb is not None and potentialNewObjs is not None and len(potentialNewObjs) > 0:
objs.extend(potentialNewObjs)
v = potentialNewVerb
if len(objs) > 0:
objs.extend(getObjsFromConjunctions(objs))
return v, objs
def getAllObjsWithAdjectives(v):
# rights is a generator
rights = list(v.rights)
objs = [tok for tok in rights if tok.dep_ in OBJECTS]
if len(objs)== 0:
objs = [tok for tok in rights if tok.dep_ in ADJECTIVES]
objs.extend(getObjsFromPrepositions(rights))
potentialNewVerb, potentialNewObjs = getObjFromXComp(rights)
if potentialNewVerb is not None and potentialNewObjs is not None and len(potentialNewObjs) > 0:
objs.extend(potentialNewObjs)
v = potentialNewVerb
if len(objs) > 0:
objs.extend(getObjsFromConjunctions(objs))
return v, objs
def findSVOs(tokens):
svos = []
verbs = [tok for tok in tokens if tok.pos_ == "VERB" and tok.dep_ != "aux"]
for v in verbs:
subs, verbNegated = getAllSubs(v)
# hopefully there are subs, if not, don't examine this verb any longer
if len(subs) > 0:
v, objs = getAllObjs(v)
for sub in subs:
for obj in objs:
objNegated = isNegated(obj)
svos.append((sub.lower_, "!" + v.lower_ if verbNegated or objNegated else v.lower_, obj.lower_))
return svos
def findSVAOs(tokens):
svos = []
verbs = [tok for tok in tokens if tok.pos_ == "VERB" and tok.dep_ != "aux"]
for v in verbs:
subs, verbNegated = getAllSubs(v)
# hopefully there are subs, if not, don't examine this verb any longer
if len(subs) > 0:
v, objs = getAllObjsWithAdjectives(v)
for sub in subs:
for obj in objs:
objNegated = isNegated(obj)
obj_desc_tokens = generate_left_right_adjectives(obj)
sub_compound = generate_sub_compound(sub)
svos.append((" ".join(tok.lower_ for tok in sub_compound), "!" + v.lower_ if verbNegated or objNegated else v.lower_, " ".join(tok.lower_ for tok in obj_desc_tokens)))
return svos
def generate_sub_compound(sub):
sub_compunds = []
for tok in sub.lefts:
if tok.dep_ in COMPOUNDS:
sub_compunds.extend(generate_sub_compound(tok))
sub_compunds.append(sub)
for tok in sub.rights:
if tok.dep_ in COMPOUNDS:
sub_compunds.extend(generate_sub_compound(tok))
return sub_compunds
def generate_left_right_adjectives(obj):
obj_desc_tokens = []
for tok in obj.lefts:
if tok.dep_ in ADJECTIVES:
obj_desc_tokens.extend(generate_left_right_adjectives(tok))
obj_desc_tokens.append(obj)
for tok in obj.rights:
if tok.dep_ in ADJECTIVES:
obj_desc_tokens.extend(generate_left_right_adjectives(tok))
return obj_desc_tokens
现在当您传递查询时，例如:
Now when you pass query such as:
from spacy.lang.en import English
parser = English()
sentence = u"""
Donald Trump is the worst president of USA, but Hillary is better than him
"""
parse = parser(sentence)
print(findSVAOs(parse))
您将获得以下内容:
[(u'donald trump', u'is', u'worst president'), (u'hillary', u'is', u'better')]
也感谢@Krzysiek 提供的解决方案，我实际上无法深入到您的库中进行修改.我宁愿尝试修改上面提到的链接来解决我的问题.
Thank you @Krzysiek for your solution too, I actually was unable to go deep into your library to modify it. I rather tried modifying the above mentioned link to solve my problem.
这篇关于如何提取句子中的主语及其相关短语?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！