提取“((Adj|Noun)+|((Adj|Noun)(Noun-Prep)?)(Adj|Noun))Noun"来自文本 (Justeson & Katz, 1995) [英] Extracting "((Adj|Noun)+|((Adj|Noun)(Noun-Prep)?)(Adj|Noun))Noun" from Text (Justeson & Katz, 1995)

问题描述

我想问一下是否可以提取((Adj|Noun)+|((Adj|Noun)(Noun-Prep)?)(Adj|Noun))Noun提出由 Justeson 和 Katz (1995) 在 R 包 openNLP 中?

I would like to query if it is possible to extract ((Adj|Noun)+|((Adj|Noun)(Noun-Prep)?)(Adj|Noun))Noun proposed by Justeson and Katz (1995) in R package openNLP?

也就是说，我想使用这种语言过滤来提取候选名词短语.

That is, I would like to use this linguistic filtering to extract candidate noun phrases.

我不太明白它的意思.

能否帮我解释一下或将这种表示形式转换为 R 语言.

Could you do me a favor to explain it or transform such representation into R language.

非常感谢.

library("openNLP")  

acq <- "This paper describes a novel optical thread plug
gauge (OTPG) for internal thread inspection using machine
vision. The OTPG is composed of a rigid industrial
endoscope, a charge-coupled device camera, and a two
degree-of-freedom motion control unit. A sequence of
partial wall images of an internal thread are retrieved and
reconstructed into a 2D unwrapped image. Then, a digital
image processing and classification procedure is used to
normalize, segment, and determine the quality of the
internal thread." 

acqTag <- tagPOS(acq)     

acqTagSplit = strsplit(acqTag," ")

我被告知要为此提出一个新问题.最初的问题是这里.

I was told to open a new question for this. The original question is here.

推荐答案

安装包:

install.packages("openNLP")
install.packages("openNLPmodels.en")

之后，你可以运行上面的代码.它将对文本中的所有单词进行 POS 标记，并将所有标记为名词、动词等的单词返回原始文本.我的示例如下:

After, you could run the above code. It will POS tag all words in the text and give back the original text with all words tagged like noun, verb etc. I this example as follows:

acqTagSplit = strsplit(acqTag," ")
> acqTag
[1] "This/DT paper/NN describes/VBZ a/DT novel/NN optical/JJ thread/NN plug/NN gauge/NN (OTPG)/NN for/IN internal/JJ thread/NN inspection/NN using/VBG machine/NN vision./NN The/DT OTPG/NNP is/VBZ composed/VBN of/IN a/DT rigid/JJ industrial/JJ endoscope,/NNS a/DT charge-coupled/JJ device/NN camera,/VBD and/CC a/DT two/CD degree-of-freedom/NN motion/NN control/NN unit./NN A/DT sequence/NN of/IN partial/JJ wall/NN images/NNS of/IN an/DT internal/JJ thread/NN are/VBP retrieved/VBN and/CC reconstructed/VBN into/IN a/DT 2D/JJ unwrapped/JJ image./NN Then,/IN a/DT digital/JJ image/NN processing/NN and/CC classification/NN procedure/NN is/VBZ used/VBN to/TO normalize,/JJ segment,/NN and/CC determine/VB the/DT quality/NN of/IN the/DT internal/JJ thread./NN"

毕竟是用破折号分隔的单词，您拥有所有 POS 标签.要将 theese 与单词分开，您可以先将单词分开 - 正如您在示例中所做的那样:

After all word, separated by a dash, you have all the POS tags. To separate theese from the word, you could first separate the words - as you did in your example:

acqTagSplit = strsplit(acqTag," ")
acqTagSplit
    [[1]]
     [1] "This/DT"              "paper/NN"             "describes/VBZ"       
     [4] "a/DT"                 "novel/NN"             "optical/JJ"          
     [7] "thread/NN"            "plug/NN"              "gauge/NN"            
    [10] "(OTPG)/NN"            "for/IN"               "internal/JJ"         
    [13] "thread/NN"            "inspection/NN"        "using/VBG"           
    [16] "machine/NN"           "vision./NN"           "The/DT"              
    [19] "OTPG/NNP"             "is/VBZ"               "composed/VBN"        
    [22] "of/IN"                "a/DT"                 "rigid/JJ"            
    [25] "industrial/JJ"        "endoscope,/NNS"       "a/DT"                
    [28] "charge-coupled/JJ"    "device/NN"            "camera,/VBD"         
    [31] "and/CC"               "a/DT"                 "two/CD"              
    [34] "degree-of-freedom/NN" "motion/NN"            "control/NN"          
    [37] "unit./NN"             "A/DT"                 "sequence/NN"         
    [40] "of/IN"                "partial/JJ"           "wall/NN"             
    [43] "images/NNS"           "of/IN"                "an/DT"               
    [46] "internal/JJ"          "thread/NN"            "are/VBP"             
    [49] "retrieved/VBN"        "and/CC"               "reconstructed/VBN"   
    [52] "into/IN"              "a/DT"                 "2D/JJ"               
    [55] "unwrapped/JJ"         "image./NN"            "Then,/IN"            
    [58] "a/DT"                 "digital/JJ"           "image/NN"            
    [61] "processing/NN"        "and/CC"               "classification/NN"   
    [64] "procedure/NN"         "is/VBZ"               "used/VBN"            
    [67] "to/TO"                "normalize,/JJ"        "segment,/NN"         
    [70] "and/CC"               "determine/VB"         "the/DT"              
    [73] "quality/NN"           "of/IN"                "the/DT"              
    [76] "internal/JJ"          "thread./NN"          

然后从 POS 标签中拆分单词:

And later split up the words from the POS tags:

strsplit(acqTagSplit[[1]], "/")

您将有一个列表，其中包含您所有带有标签的单词，并且在里面首先将单词与标签分开.见:

You will have a list, which contains all of your words with the tags, and inside first have the word and after the tag separated. See:

str(strsplit(acqTagSplit[[1]], "/"))
List of 77
 $ : chr [1:2] "This" "DT"
 $ : chr [1:2] "paper" "NN"
 $ : chr [1:2] "describes" "VBZ"
 $ : chr [1:2] "a" "DT"
 $ : chr [1:2] "novel" "NN"
 $ : chr [1:2] "optical" "JJ"
 $ : chr [1:2] "thread" "NN"
 $ : chr [1:2] "plug" "NN"
 $ : chr [1:2] "gauge" "NN"
 $ : chr [1:2] "(OTPG)" "NN"
 $ : chr [1:2] "for" "IN"
 $ : chr [1:2] "internal" "JJ"
 $ : chr [1:2] "thread" "NN"
 $ : chr [1:2] "inspection" "NN"
 $ : chr [1:2] "using" "VBG"
 $ : chr [1:2] "machine" "NN"
 $ : chr [1:2] "vision." "NN"
 $ : chr [1:2] "The" "DT"
 $ : chr [1:2] "OTPG" "NNP"
 $ : chr [1:2] "is" "VBZ"
 $ : chr [1:2] "composed" "VBN"
 $ : chr [1:2] "of" "IN"
 $ : chr [1:2] "a" "DT"
 $ : chr [1:2] "rigid" "JJ"
 $ : chr [1:2] "industrial" "JJ"
 $ : chr [1:2] "endoscope," "NNS"
 $ : chr [1:2] "a" "DT"
 $ : chr [1:2] "charge-coupled" "JJ"
 $ : chr [1:2] "device" "NN"
 $ : chr [1:2] "camera," "VBD"
 $ : chr [1:2] "and" "CC"
 $ : chr [1:2] "a" "DT"
 $ : chr [1:2] "two" "CD"
 $ : chr [1:2] "degree-of-freedom" "NN"
 $ : chr [1:2] "motion" "NN"
 $ : chr [1:2] "control" "NN"
 $ : chr [1:2] "unit." "NN"
 $ : chr [1:2] "A" "DT"
 $ : chr [1:2] "sequence" "NN"
 $ : chr [1:2] "of" "IN"
 $ : chr [1:2] "partial" "JJ"
 $ : chr [1:2] "wall" "NN"
 $ : chr [1:2] "images" "NNS"
 $ : chr [1:2] "of" "IN"
 $ : chr [1:2] "an" "DT"
 $ : chr [1:2] "internal" "JJ"
 $ : chr [1:2] "thread" "NN"
 $ : chr [1:2] "are" "VBP"
 $ : chr [1:2] "retrieved" "VBN"
 $ : chr [1:2] "and" "CC"
 $ : chr [1:2] "reconstructed" "VBN"
 $ : chr [1:2] "into" "IN"
 $ : chr [1:2] "a" "DT"
 $ : chr [1:2] "2D" "JJ"
 $ : chr [1:2] "unwrapped" "JJ"
 $ : chr [1:2] "image." "NN"
 $ : chr [1:2] "Then," "IN"
 $ : chr [1:2] "a" "DT"
 $ : chr [1:2] "digital" "JJ"
 $ : chr [1:2] "image" "NN"
 $ : chr [1:2] "processing" "NN"
 $ : chr [1:2] "and" "CC"
 $ : chr [1:2] "classification" "NN"
 $ : chr [1:2] "procedure" "NN"
 $ : chr [1:2] "is" "VBZ"
 $ : chr [1:2] "used" "VBN"
 $ : chr [1:2] "to" "TO"
 $ : chr [1:2] "normalize," "JJ"
 $ : chr [1:2] "segment," "NN"
 $ : chr [1:2] "and" "CC"
 $ : chr [1:2] "determine" "VB"
 $ : chr [1:2] "the" "DT"
 $ : chr [1:2] "quality" "NN"
 $ : chr [1:2] "of" "IN"
 $ : chr [1:2] "the" "DT"
 $ : chr [1:2] "internal" "JJ"
 $ : chr [1:2] "thread." "NN"

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