问题描述
我想查询是否可以提取建议的((Adj | Noun)+ |((Adj | Noun)(Noun-Prep)?)(Adj | Noun))名词由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.
我不太明白它的含义.
I cannot well understand its meaning.
您能帮我解释一下它还是将这种表示形式转换成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," ")
有人告诉我为此打开一个新问题.原始问题是此处.
I was told to open a new question for this. The original question is here.
推荐答案
通过以下方式安装软件包:
Installing the package by:
install.packages("openNLP")
install.packages("openNLPmodels.en")
之后，您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，并返回带有所有标记为名词，动词等词的原始文本.在本示例中，如下所示:
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"
在所有单词之后，用短划线隔开，您便拥有了所有POS标签.要将泰斯语与单词分开，您可以先将单词分开-就像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，其中包含所有带有标签的单词，并且内部第一个单词带有单词，且在标签之后.参见:
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"
这篇关于提取“(((Adj | Noun)+ |((Adj | Noun)(Noun-Prep)?)(Adj | Noun))Noun".摘自Text(Justeson& Katz，1995)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！