結合nltk.RegexpParser語法

正如我對學習更多關於NLP下一步，我想實現一個簡單的啓發式改善超出了簡單的n-gram結果。結合nltk.RegexpParser語法

根據下面鏈接的斯坦福搭配PDF，他們提到通過「只通過可能成爲」短語「的那些模式的部分語音過濾器傳遞」候選短語「將產生比簡單地使用最頻繁的結果更好的結果存在的雙克來源：搭配，第143頁 - 144：https://nlp.stanford.edu/fsnlp/promo/colloc.pdf

144頁上的表中有7個標籤圖案在順序，NLTK POS標籤等效爲：

JJ NN

。 NN

JJ JJ NN

JJ NN NN

NN JJ NN

NN NN NN

NN IN NN

在下面的代碼，我可以得到所希望的結果時我獨立以下應用每個語法。但是，當我嘗試合併相同的語法時，我沒有收到預期的結果。

在我的代碼，你可以看到，我去掉一個句子中，取消1個語法，運行它，並檢查結果。

我應該能夠通過合併語法（只是在下面的代碼，其中3）所有的句子組合，運行它，並得到想要的結果。

我的問題是，我該如何正確地結合語法？

我假設，結合語法就像是一個「OR」，發現這個圖案，或者這種模式...

在此先感謝。

import nltk 

# The following sentences are correctly grouped with <JJ>*<NN>+. 
# Should see: 'linear function', 'regression coefficient', 'Gaussian random variable' and 
# 'cumulative distribution function' 
SampleSentence = "In mathematics, the term linear function refers to two distinct, although related, notions" 
#SampleSentence = "The regression coefficient is the slope of the line of the regression equation." 
#SampleSentence = "In probability theory, Gaussian random variable is a very common continuous probability distribution." 
#SampleSentence = "In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x." 

# The following sentences are correctly grouped with <NN.?>*<V.*>*<NN> 
# Should see 'mean squared error' and # 'class probability function'. 
#SampleSentence = "In statistics, the mean squared error (MSE) of an estimator measures the average of the squares of the errors, that is, the difference between the estimator and what is estimated." 
#SampleSentence = "The class probability function is interesting" 

# The sentence below is correctly grouped with <NN.?>*<IN>*<NN.?>*. 
# should see 'degrees of freedom'. 
#SampleSentence = "In statistics, the degrees of freedom is the number of values in the final calculation of a statistic that are free to vary." 

SampleSentence = SampleSentence.lower() 

print("\nFull sentence: ", SampleSentence, "\n") 

tokens = nltk.word_tokenize(SampleSentence) 
textTokens = nltk.Text(tokens)  

# Determine the POS tags. 
POStagList = nltk.pos_tag(textTokens)  

# The following grammars work well *independently* 
grammar = "NP: {<JJ>*<NN>+}" 
#grammar = "NP: {<NN.?>*<V.*>*<NN>}"  
#grammar = "NP: {<NN.?>*<IN>*<NN.?>*}" 


# Merge several grammars above into a single one below. 
# Note that all 3 correct grammars above are included below. 

''' 
grammar = """ 
      NP: 
       {<JJ>*<NN>+} 
       {<NN.?>*<V.*>*<NN>} 
       {<NN.?>*<IN>*<NN.?>*} 
     """ 
''' 

cp = nltk.RegexpParser(grammar) 

result = cp.parse(POStagList) 

for subtree in result.subtrees(filter=lambda t: t.label() == 'NP'): 
    print("NP Subtree:", subtree)

來源

2017-10-08 RandomTask

如果你能幫助我瞭解更多，你不想寫3個獨立的行這樣的語法= 「」」 NP： { * +} { * * } { * * *} 「」「。相反，你需要一個單行的正則表達式模式，可以容納所有3種模式。 –

嗨拉胡爾。我想，讓他們產生，他們分別產生相同的結果以某種方式結合3種正則表達式模式。我很公正，如何用1，2，3以上的線條寫出來。我會在接下來的幾天嘗試下面的代碼。謝謝。 – RandomTask

當然，繼續！我已經嘗試過多種場景，並且它已成功。嘗試並回到其他任何問題 –

如果我的意見是，你在找什麼，那麼下面就是答案：

grammar = """ 
      NP: 
       {<JJ>*<NN.?>*<V.|IN>*<NN.?>*}"""

來源

2017-10-17 09:07:39

結合nltk.RegexpParser語法

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