如何使用词嵌入作为 CRF (sklearn-crfsuite) 模型训练的特征

Question

我想开发一个 NER 模型，我想在其中使用词嵌入功能来训练 CRF 模型。代码在没有词嵌入功能的情况下可以完美运行，但是当我将嵌入作为 CRF 训练的功能插入时，收到错误消息。这是我的代码片段的一部分：

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')

from itertools import chain

import nltk
import sklearn
import scipy.stats
from sklearn.metrics import make_scorer
#from sklearn.cross_validation import cross_val_score
#from sklearn.grid_search import RandomizedSearchCV

import sklearn_crfsuite
from sklearn_crfsuite import scorers
from sklearn_crfsuite import metrics
import pickle
from gensim.models import KeyedVectors
import numpy as np
# Load vectors directly from the file
model1 = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) ### Loading pre-trainned word2vec model
### Embedding function 
def get_features(word):
    word=word.lower()
    vectors=[]
    try:
        vectors.append(model1[word])
    except:
        pass
    #vectors=np.array(vectors)
    #vectors=vectors[0]
    return vectors

def word2features(sent, i):
    word = sent[i][0]
    wordembdding=get_features(word)   ## word embedding vector 
    wordembdding=np.array(wordembdding) ## vectors 
    #wordembdding= 
    #wordembdding=wordembdding[0]
    postag = sent[i][1]
    tag1=sent[i][2]
    tag2=sent[i][4]
    tag3 = sent[i][5]


    features = {
        'bias': 1.0,
        'word.lower()': word.lower(),
        'word[-3:]': word[-3:],
        'word[-2:]': word[-2:],
        'wordembdding': wordembdding,
        'word.isupper()': word.isupper(),
        'word.istitle()': word.istitle(),
        'word.isdigit()': word.isdigit(),
        'postag': postag,
        'postag[:2]': postag[:2],
        'tag1': tag1,
        'tag1[:2]': tag1[:2],
        'tag2': tag2,
        'tag2[:2]': tag2[:2],
        'tag3': tag3,
        'tag3[:2]': tag3[:2],
        'wordlength': len(word),
        'wordinitialcap': word[0].isupper(),
        'wordmixedcap': len([x for x in word[1:] if x.isupper()])>0,
        'wordallcap': len([x for x in word if x.isupper()])==len(word),
        'distfromsentbegin': i
    }
    if i > 0:
        word1 = sent[i-1][0]
        wordembdding1= get_features(word1)
        wordembdding1=np.array(wordembdding1)
        #wordembdding1=f2(wordembdding1)
        postag1 = sent[i-1][1]
        tag11=sent[i-1][2]
        tag22=sent[i-1][4]
        tag33 = sent[i-1][5]
        features.update({
            '-1:word.lower()': word1.lower(),
            '-1:word.istitle()': word1.istitle(),
            '-1:word.isupper()': word1.isupper(),
            '-1:wordembdding': wordembdding1,   # word embedding features 
            '-1:postag': postag1,
            '-1:postag[:2]': postag1[:2],
            '-1:tag1': tag1,
            '-1:tag1[:2]': tag1[:2],
            '-1:tag2': tag2,
            '-1:tag2[:2]': tag2[:2],
            '-1:tag3': tag3,
            '-1:tag3[:2]': tag3[:2],
            '-1:wordlength': len(word),
            '-1:wordinitialcap': word[0].isupper(),
            '-1:wordmixedcap': len([x for x in word[1:] if x.isupper()])>0,
            '-1:wordallcap': len([x for x in word if x.isupper()])==len(word),
        })
    else:
        features['BOS'] = True

    if i < len(sent)-1:
        word1 = sent[i+1][0]
        wordembdding1= get_features(word1)
        wordembdding1= get_features(word1)
        wordembdding1=np.array(wordembdding1) ## word embedding features 
        #wordembdding1=f2(wordembdding)
        postag1 = sent[i+1][1]
        tag11=sent[i+1][2]
        tag22=sent[i+1][4]
        tag33 = sent[i+1][5]
        features.update({
            '+1:word.lower()': word1.lower(),
            '+1:word.istitle()': word1.istitle(),
            '+1:word.isupper()': word1.isupper(),
            '+1:wordembdding': wordembdding1,
            '+1:postag': postag1,
            '+1:postag[:2]': postag1[:2],
            '+1:tag1': tag1,
            '+1:tag1[:2]': tag1[:2],
            '+1:tag2': tag2,
            '+1:tag2[:2]': tag2[:2],
            '+1:tag3': tag3,
            '+1:tag3[:2]': tag3[:2],
            '+1:wordlength': len(word),
            '+1:wordinitialcap': word[0].isupper(),
            '+1:wordmixedcap': len([x for x in word[1:] if x.isupper()])>0,
            '+1:wordallcap': len([x for x in word if x.isupper()])==len(word),
        })
    else:
        features['EOS'] = True

    return features


def sent2features(sent):
    return [word2features(sent, i) for i in range(len(sent))]

def sent2labels(sent):
    return [label for token, postag, tag1, label, tag2, tag3 in sent]

def sent2tokens(sent):
    return [token for token, postag, tag1, label, tag2, tag3, tag4, tag5 in sent]



X_train = [sent2features(s) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]

X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]


%%time
crf = sklearn_crfsuite.CRF(
    algorithm='lbfgs',
    c1=0.1,
    c2=0.1,
    max_iterations=100,
    all_possible_transitions=True
)
crf.fit(X_train, y_train)   ### Error message when try to train

当我想训练 CRF 模型时，我收到以下错误消息：

TypeError: only size-1 arrays can be converted to Python scalars

谁能建议我如何使用词嵌入向量来训练 CRF 模型？

Answer 1

如您所见here，目前 python-crfsuite 和 sklearn-crfsuite 没有支持数组功能，如词嵌入。

相反，您可以将每个矢量分量作为特征传递。

{...
 'v0': 1.81583762e-02,
 'v1': 2.83553465e-02,
  ...
 'v299': -4.26079705e-02,
 ...}

我建议替换你的 get_features 函数：

def get_features(word):
    word=word.lower()
    try:
         vector=model1[word]
    except:
        # if the word is not in vocabulary,
        # returns zeros array
        vector=np.zeros(300,)

    return vector

然后修改 word2features 函数，return 为向量的每个分量添加一个新特征：

def word2features(sent, i):
    word = sent[i][0]
    wordembdding=get_features(word)   ## word embedding vector 
    postag = sent[i][1]
    tag1=sent[i][2]
    tag2=sent[i][4]
    tag3 = sent[i][5]


    features = {
        'bias': 1.0,
        'word.lower()': word.lower(),
        'word[-3:]': word[-3:],
        'word[-2:]': word[-2:],
        'word.isupper()': word.isupper(),
        'word.istitle()': word.istitle(),
        'word.isdigit()': word.isdigit(),
        'postag': postag,
        'postag[:2]': postag[:2],
        'tag1': tag1,
        'tag1[:2]': tag1[:2],
        'tag2': tag2,
        'tag2[:2]': tag2[:2],
        'tag3': tag3,
        'tag3[:2]': tag3[:2],
        'wordlength': len(word),
        'wordinitialcap': word[0].isupper(),
        'wordmixedcap': len([x for x in word[1:] if x.isupper()])>0,
        'wordallcap': len([x for x in word if x.isupper()])==len(word),
        'distfromsentbegin': i
    }

    # here you add 300 features (one for each vector component)
    for iv,value in enumerate(wordembdding):
        features['v{}'.format(iv)]=value

# And so on...

两个小记：

如果你的文本中有很多单词，这些单词不在词汇表中，那么单词嵌入不能对你的 NER 模型有太大的改进。也许你可以使用 Fasttext（也集成在 Gensim 中），它可以正确处理看不见的单词。
即使有用，为每个单词添加向量嵌入也会使您的训练集变得非常大，产生很长的训练时间和非常大的分类器。

如何使用词嵌入作为 CRF (sklearn-crfsuite) 模型训练的特征

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