结果Stanford NER tagger NLTK(python)与JAVA的差异

我使用python和java来运行斯坦福NER标记器,但我看到结果的差异.

例如,当我输入句子"参与使用ERwin作为主要软件的数据建模的所有方面.",

JAVA结果:

"ERwin": "PERSON"

Python结果:

In [6]: NERTagger.tag("Involved in all aspects of data modeling using ERwin as the primary software for this.".split())
Out [6]:[(u'Involved', u'O'),
 (u'in', u'O'),
 (u'all', u'O'),
 (u'aspects', u'O'),
 (u'of', u'O'),
 (u'data', u'O'),
 (u'modeling', u'O'),
 (u'using', u'O'),
 (u'ERwin', u'O'),
 (u'as', u'O'),
 (u'the', u'O'),
 (u'primary', u'O'),
 (u'software', u'O'),
 (u'for', u'O'),
 (u'this.', u'O')]

Python nltk包装器无法将"ERwin"作为PERSON捕获.

这里有趣的是Python和Java使用2015-04-20发布的相同训练数据(english.all.3class.caseless.distsim.crf.ser.gz).

我的最终目标是让python以与Java相同的方式工作.

我在nltk.tag中查看StanfordNERTagger,看看有什么我可以修改的.下面是包装代码:

class StanfordNERTagger(StanfordTagger):
"""
A class for Named-Entity Tagging with Stanford Tagger. The input is the paths to:

- a model trained on training data
- (optionally) the path to the stanford tagger jar file. If not specified here,
  then this jar file must be specified in the CLASSPATH envinroment variable.
- (optionally) the encoding of the training data (default: UTF-8)

Example:

    >>> from nltk.tag import StanfordNERTagger
    >>> st = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz') # doctest: +SKIP
    >>> st.tag('Rami Eid is studying at Stony Brook University in NY'.split()) # doctest: +SKIP
    [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'),
     ('at', 'O'), ('Stony', 'ORGANIZATION'), ('Brook', 'ORGANIZATION'),
     ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'LOCATION')]
"""

_SEPARATOR = '/'
_JAR = 'stanford-ner.jar'
_FORMAT = 'slashTags'

def __init__(self, *args, **kwargs):
    super(StanfordNERTagger, self).__init__(*args, **kwargs)

@property
def _cmd(self):
    # Adding -tokenizerFactory edu.stanford.nlp.process.WhitespaceTokenizer -tokenizerOptions tokenizeNLs=false for not using stanford Tokenizer  
    return ['edu.stanford.nlp.ie.crf.CRFClassifier',
            '-loadClassifier', self._stanford_model, '-textFile',
            self._input_file_path, '-outputFormat', self._FORMAT, '-tokenizerFactory', 'edu.stanford.nlp.process.WhitespaceTokenizer', '-tokenizerOptions','\"tokenizeNLs=false\"']

def parse_output(self, text, sentences):
    if self._FORMAT == 'slashTags':
        # Joint together to a big list    
        tagged_sentences = []
        for tagged_sentence in text.strip().split("\n"):
            for tagged_word in tagged_sentence.strip().split():
                word_tags = tagged_word.strip().split(self._SEPARATOR)
                tagged_sentences.append((''.join(word_tags[:-1]), word_tags[-1]))

        # Separate it according to the input
        result = []
        start = 0 
        for sent in sentences:
            result.append(tagged_sentences[start:start + len(sent)])
            start += len(sent);
        return result 

    raise NotImplementedError

或者,如果是因为使用了不同的分类器(在java代码中,它似乎使用AbstractSequenceClassifier,另一方面,python nltk包装器使用CRFClassifier.)有没有一种方法可以在python包装器中使用AbstractSequenceClassifier？

尝试maxAdditionalKnownLCWords在CoreNLP的属性文件(或命令行)中设置为0,如果可能,也尝试为NLTK 设置为0.这会禁用一个选项,允许NER系统稍微从测试时间数据中学习,这可能会导致偶尔出现略微不同的结果.