NER学习系列之-BILSTM+CRF

做了一段时间的Sequence Labeling的工作，发现在NER任务上面，很多论文都采用LSTM-CRFs的结构。CRF在最后一层应用进来可以考虑到概率最大的最优label路径，可以提高指标。

一般的深度学习框架是没有CRF layer的，需要手动实现。最近在学习PyTorch，里面有一个Bi-LSTM-CRF的tutorial实现。不得不说PyTorch的tutorial真是太良心了，基本涵盖了NLP领域各个流行的model实现。在这里从头梳理一遍，也记录下学习过程中的一些问题。

 

Bi-LSTM-CRF的结构一般如上，最后一层利用CRF来学习一个最优路径。Bi-LSTM layer的输出维度是tag size，这就相当于是每个词映射到tag的发射概率值，设Bi-LSTM的输出矩阵为，其中代表词映射到的非归一化概率。对于CRF来说，我们假定存在一个转移矩阵，则代表转移到的转移概率。

 

对于输入序列对应的输出tag序列，定义分数为

 

利用Softmax函数，我们为每一个正确的tag序列定义一个概率值（代表所有的tag序列，包括不可能出现的）

 

 

因而在训练中，我们只需要最大化似然概率即可，这里我们利用对数似然

 

所以我们将损失函数定义为，就可以利用梯度下降法来进行网络的学习了。

在对损失函数进行计算的时候，的计算很简单，而（下面记作logsumexp）的计算稍微复杂一些，因为需要计算每一条可能路径的分数。这里用一种简便的方法，对于到词的路径，可以先把到词的logsumexp计算出来，因为

 

因此先计算每一步的路径分数和直接计算全局分数相同，但这样可以大大减少计算的时间。下面是PyTorch中的代码

 

def _forward_alg(self, feats):
    # Do the forward algorithm to compute the partition function
    init_alphas = torch.Tensor(1, self.tagset_size).fill_(-10000.)
    # START_TAG has all of the score.
    init_alphas[0][self.tag_to_ix[START_TAG]] = 0.

    # Wrap in a variable so that we will get automatic backprop
    forward_var = autograd.Variable(init_alphas)

    # Iterate through the sentence
    for feat in feats:
        alphas_t = []  # The forward variables at this timestep
        for next_tag in range(self.tagset_size):
            # broadcast the emission score: it is the same regardless of
            # the previous tag
            emit_score = feat[next_tag].view(
                1, -1).expand(1, self.tagset_size)
            # the ith entry of trans_score is the score of transitioning to
            # next_tag from i
            trans_score = self.transitions[next_tag].view(1, -1)
            # The ith entry of next_tag_var is the value for the
            # edge (i -> next_tag) before we do log-sum-exp
            next_tag_var = forward_var + trans_score + emit_score
            # The forward variable for this tag is log-sum-exp of all the
            # scores.
            alphas_t.append(log_sum_exp(next_tag_var))
        forward_var = torch.cat(alphas_t).view(1, -1)
    terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
    alpha = log_sum_exp(terminal_var)
    return alpha

在解码时，采用Viterbi算法

def _viterbi_decode(self, feats):
    backpointers = []

    # Initialize the viterbi variables in log space
    init_vvars = torch.Tensor(1, self.tagset_size).fill_(-10000.)
    init_vvars[0][self.tag_to_ix[START_TAG]] = 0

    # forward_var at step i holds the viterbi variables for step i-1
    forward_var = autograd.Variable(init_vvars)
    for feat in feats:
        bptrs_t = []  # holds the backpointers for this step
        viterbivars_t = []  # holds the viterbi variables for this step

        for next_tag in range(self.tagset_size):
            # next_tag_var[i] holds the viterbi variable for tag i at the
            # previous step, plus the score of transitioning
            # from tag i to next_tag.
            # We don't include the emission scores here because the max
            # does not depend on them (we add them in below)
            next_tag_var = forward_var + self.transitions[next_tag]
            best_tag_id = argmax(next_tag_var)
            bptrs_t.append(best_tag_id)
            viterbivars_t.append(next_tag_var[0][best_tag_id])
        # Now add in the emission scores, and assign forward_var to the set
        # of viterbi variables we just computed
        forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
        backpointers.append(bptrs_t)

    # Transition to STOP_TAG
    terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
    best_tag_id = argmax(terminal_var)
    path_score = terminal_var[0][best_tag_id]

    # Follow the back pointers to decode the best path.
    best_path = [best_tag_id]
    for bptrs_t in reversed(backpointers):
        best_tag_id = bptrs_t[best_tag_id]
        best_path.append(best_tag_id)
    # Pop off the start tag (we dont want to return that to the caller)
    start = best_path.pop()
    assert start == self.tag_to_ix[START_TAG]  # Sanity check
    best_path.reverse()
    return path_score, best_path

全部代码实现可以移步Bi-LSTM-CRF。

参考

Bidirectional LSTM-CRF Models for Sequence Tagging
Neural Architectures for Named Entity Recognition
Advanced: Making Dynamic Decisions and the Bi-LSTM CRF

另外一个参考资料：

零基础入门——NER命名实体识别(BI-LSTM+CRF):https://blog.csdn.net/buppt/article/details/81180361

和 https://blog.csdn.net/cuihuijun1hao/article/details/79405740