python--stanfordcorenlp

stanford core nlp 是一個(gè)用于nlp的工具庫(kù)。它是用java寫的镇草，但是現(xiàn)在也為python提供了接口。前段時(shí)間筆者嘗試在python中使用它：
首先引入stanfordcorenlp的包
在python文件中引用：

from stanfordcorenlp import StanfordCoreNLP

stanfordcorenlp 中只有 StanfordCoreNLP 一個(gè)類
獲得StanfordCoreNLP 的對(duì)象：
創(chuàng)建StanfordCoreNLP 對(duì)象需要傳入一個(gè)路徑參數(shù)瘤旨，從而獲得一個(gè)存放相應(yīng)jar包的文件夾：該文件夾下載地址：https://stanfordnlp.github.io/CoreNLP/download.html
筆者使用的是：stanford-corenlp-full-2016-10-31

nlp = StanfordCoreNLP(path)  # 這里的path即是stanford-corenlp-full-2016-10-31 的路徑

使用
它的使用非常簡(jiǎn)單

from stanfordcorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP(path)
sentence = "i 've had the player for about 2 years now and it still performs nicely with the exception of an occasional wwhhhrrr sound from the motor ."
print(nlp.dependency_parse(sentence))
nlp.close()

但是直接運(yùn)行會(huì)出錯(cuò)

$ python WordFormation.py
Traceback (most recent call last):
  File "WordFormation.py", line 1, in <module>
    from stanfordcorenlp import StanfordCoreNLP
ModuleNotFoundError: No module named 'stanfordcorenlp'

或是：

PermissionError: [Errno 1] Operation not permitted

因此使用root權(quán)限運(yùn)行：成功獲得dependency

$ sudo python WordFormation.py
[('ROOT', 0, 3), ('nsubj', 3, 1), ('aux', 3, 2), ('det', 5, 4), ('dobj', 3, 5), ('case', 9, 6), ('advmod', 8, 7), ('nummod', 9, 8), ('nmod', 5, 9), ('advmod', 3, 10), ('cc', 3, 11), ('nsubj', 14, 12), ('advmod', 14, 13), ('conj', 3, 14), ('advmod', 14, 15), ('case', 18, 16), ('det', 18, 17), ('nmod', 14, 18), ('case', 23, 19), ('det', 23, 20), ('amod', 23, 21), ('compound', 23, 22), ('nmod', 18, 23), ('case', 26, 24), ('det', 26, 25), ('nmod', 23, 26), ('punct', 3, 27)]

其中的那些數(shù)字代表的是第幾個(gè)單詞梯啤，但是它是從1開始數(shù)的，('ROOT', 0, 3) 中的0不代表sentence中的單詞

StanfordCoreNLP 還有一些功能存哲，比如詞性標(biāo)注等都可以使用

但是筆者沒有從StanfordCoreNLP 類中獲得可以進(jìn)一步獲得dependency的方法：比如復(fù)合名詞修飾 nmod 在這里我只能獲得 nmod 而不能獲得修飾用的介詞 nmod:for 的形式

筆者沒能找到合適的方法因宇，因此我決定改用java嘗試一下

JAVA--stanfordcorenlp

java 的話，語(yǔ)句相應(yīng)會(huì)復(fù)雜一些

首先引入相應(yīng)的jar包：
由于筆者建的maven項(xiàng)目
pom.xml 中加入：

    <properties>
        <corenlp.version>3.9.2</corenlp.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>edu.stanford.nlp</groupId>
            <artifactId>stanford-corenlp</artifactId>
            <version>${corenlp.version}</version>
        </dependency>

        <dependency>
            <groupId>edu.stanford.nlp</groupId>
            <artifactId>stanford-corenlp</artifactId>
            <version>${corenlp.version}</version>
            <classifier>models</classifier>
        </dependency>

    </dependencies>

開始運(yùn)行：

import edu.stanford.nlp.ling.CoreAnnotations;
import java.util.Properties;
public class StanfordEnglishNlpExample {
    public static void main(String[] args) {
        Properties props = new Properties();
        // 設(shè)置相應(yīng)的properties
        props.put("annotators", "tokenize,ssplit,pos,parse,depparse");
        props.put("tokenize.options", "ptb3Escaping=false");
        props.put("parse.maxlen", "10000");
        props.put("depparse.extradependencies", "SUBJ_ONLY");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props); // 獲得StanfordCoreNLP 對(duì)象

        String str = "i 've had the player for about 2 years now and it still performs nicely with the exception of an occasional wwhhhrrr sound from the motor .";
        Annotation document = new Annotation(str);
        pipeline.annotate(document);
        CoreMap sentence = document.get(CoreAnnotations.SentencesAnnotation.class).get(0);
        SemanticGraph dependency_graph = sentence.get(SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation.class);  // 獲得依賴關(guān)系圖
        System.out.println("\n\nDependency Graph: " + dependency_graph.toString(SemanticGraph.OutputFormat.LIST));
    }// 直接打印關(guān)系
}

獲得結(jié)果：

Dependency Graph: root(ROOT-0, had-3)
nsubj(had-3, i-1)
aux(had-3, 've-2)
det(player-5, the-4)
dobj(had-3, player-5)
case(years-9, for-6)
advmod(2-8, about-7)
nummod(years-9, 2-8)
nmod:for(player-5, years-9)
advmod(had-3, now-10)
cc(had-3, and-11)
nsubj(performs-14, it-12)
advmod(performs-14, still-13)
conj:and(had-3, performs-14)
advmod(performs-14, nicely-15)
case(exception-18, with-16)
det(exception-18, the-17)
nmod:with(performs-14, exception-18)
case(sound-23, of-19)
det(sound-23, an-20)
amod(sound-23, occasional-21)
compound(sound-23, wwhhhrrr-22)
nmod:of(exception-18, sound-23)
case(motor-26, from-24)
det(motor-26, the-25)
nmod:from(sound-23, motor-26)
punct(had-3, .-27)

在這里面就可以看到nmod:with nmod:of 這樣的依存關(guān)系了

但是其實(shí)還是有問題的：
對(duì)于以上代碼中的SemanticGraph 對(duì)象 dependency_graph 來(lái)說(shuō)
如果想要獲得它的對(duì)象的依存關(guān)系

List<SemanticGraphEdge> list = dependencies.edgeListSorted();

這時(shí)候就會(huì)發(fā)現(xiàn)祟偷，這個(gè)list中沒有root的關(guān)系
實(shí)際上察滑，如果想要root 關(guān)系，只能通過從 dependency_graph 再獲取root關(guān)系列表修肠，這樣的話贺辰，沒有很好的順序關(guān)系

因此用另一種方法來(lái)獲得：
為了將工作做的更完整一些，這里筆者將完成詞性標(biāo)注工作
想要使用詞性標(biāo)注器氛赐，首先需要獲得english-left3words-distsim.tagger文件魂爪，這個(gè)文件在stanford-corenlp-2016-10-31 中有，可以直接用艰管。但是很有可能由于引用的jar包和使用的tagger文件的版本不一致導(dǎo)致錯(cuò)誤滓侍。

實(shí)際上，在我們引入的stanford-corenlp-models的jar包里就有這個(gè)tagger文件牲芋，但是想要將它讀出來(lái)需要一點(diǎn)工作

URL url = new URL("jar:file:"+ path +
                "!/edu/stanford/nlp/models/pos-tagger/english-left3words/" +
                "english-left3words-distsim.tagger"); 
# 這里的path是jar包的路徑撩笆，捺球！后面的是tagger文件在jar包內(nèi)部路徑
JarURLConnection jarURLConnection = (JarURLConnection) url.openConnection();

由于詞性標(biāo)注器，MaxentTagger 類構(gòu)造器夕冲，可以傳入路徑氮兵，也可以傳入InputStream 對(duì)象：

MaxentTagger tagger = new MaxentTagger(jarURLConnection.getInputStream());

成功獲得對(duì)象：

    public static void main(String[] args) throws java.net.MalformedURLException, IOException {

        URL url = new URL("jar:file:"+ path +
                "!/edu/stanford/nlp/models/pos-tagger/english-left3words/" +
                "english-left3words-distsim.tagger");
        JarURLConnection jarURLConnection = (JarURLConnection) url.openConnection();

        MaxentTagger tagger = new MaxentTagger(jarURLConnection.getInputStream());
        DependencyParser parser = DependencyParser.loadFromModelFile(DependencyParser.DEFAULT_MODEL); // 依存關(guān)系解析器

        String review = "i 've had the player for about 2 years now and it still performs nicely with the exception of an occasional wwhhhrrr sound from the motor .";
        String result = "[";
        DocumentPreprocessor tockenizer = new DocumentPreprocessor(new StringReader(review)); // 將一段話，分成多個(gè)句子
        for(List<HasWord> sentence: tockenizer){
            List<TaggedWord> tagged = tagger.tagSentence(sentence); // 對(duì)句子中的詞打標(biāo)簽
            GrammaticalStructure gs = parser.predict(tagged);
            List<TypedDependency> tdl = gs.typedDependenciesCCprocessed(); // 獲得依賴關(guān)系
            for(TypedDependency td: tdl){
                result = result.concat(td.reln()+"("+td.gov()+", "+td.dep()+"),");
            }
        }
        System.out.println(result.substring(0,result.length()-1)+"]");
    }

獲得結(jié)果：

[nsubj(had/VBD, i/FW),aux(had/VBD, 've/VBP),root(ROOT, had/VBD),det(player/NN, the/DT),dobj(had/VBD, player/NN),case(years/NNS, for/IN),advmod(2/CD, about/IN),nummod(years/NNS, 2/CD),nmod:for(player/NN, years/NNS),advmod(had/VBD, now/RB),cc(had/VBD, and/CC),nsubj(performs/VBZ, it/PRP),advmod(performs/VBZ, still/RB),conj:and(had/VBD, performs/VBZ),advmod(performs/VBZ, nicely/RB),case(exception/NN, with/IN),det(exception/NN, the/DT),nmod:with(performs/VBZ, exception/NN),case(sound/NN, of/IN),det(sound/NN, an/DT),amod(sound/NN, occasional/JJ),compound(sound/NN, wwhhhrrr/NN),nmod:of(exception/NN, sound/NN),case(motor/NN, from/IN),det(motor/NN, the/DT),nmod:from(sound/NN, motor/NN),punct(had/VBD, ./.)]