动态TopicModel BERTopic 中文 长文本 SentenceTransformer BERT 均值特征向量 整体特征分词关键词

动态TopicModel BERTopic 中文 长文本 SentenceTransformer BERT 均值特征向量 整体特征分词Topic

主题模型与BERTopic

主题模型Topic Model最常用的算法是LDA隐含迪利克雷分布，然而LDA有很多缺陷，如：

1. LDA需要主题数量作为输入，非常依赖这个值；
2. LDA存在长尾问题，对于大量低频词数据集表现不好；
3. LDA只考虑词频，没有考虑词与词之间的关系；
4. LDA不考虑时间信息，难以应用到动态主题模型任务。

为了解决这些问题，学界提出了DTM、ETM、DETM、BERTopic等方法，其中BERTopic是近年提出的热度很高的方法，它主要思路是寻找文本整体的BERT特征向量，然后对各文本特征在样本空间中做聚类，找到Topic，然后基于TF-IDF模型寻找每个Topic的关键词，最后寻找Topic在每个时间段的关键词表示。
然而BERTopic也存在几个问题：

1. BERTopic本身是为英文任务设计的，不适应于中文任务，因为英文无需分词，词与词之间天然用空格隔开，BERTopic对英文文本直接提取BERT特征，然后在空格隔开的词上找每个Topic的关键词，很便捷；对于中文来说，中文是需要分词的，如果对中文文本整体提取特征，就需要在中文的分词结果上提取每个Topic的关键词；
2. 由于提取的是BERT特征，BERT本身要求文本长度不超过512，否则就会截断，对于这个问题，BERTopic里面是直接进行了截断，然而这种方法并不很合适，对长文本不太友好；

分别针对这两个问题，本文做了两个改进：

在文本整体上提取特征，在分词结果上提取关键词

改法很简单，调用topic_model.fit_transform()时，同时传入原始文本和分词（以及去停用词）结果，修改_bertopic.py中的源码，主要是改fit_transform()函数；

对文本的每512个字符提取BERT特征，然后求均值作为文本特征

改法很简单，经过读源码可知主要是SenteTransformer包里的SentenceTransformer.py里的encode()函数在进行特征提取，然后更改一下这个函数，更改为如下结果：

    def encode(self, sentences: Union[str, List[str]],
               batch_size: int = 1,
               show_progress_bar: bool = None,
               output_value: str = 'sentence_embedding',
               convert_to_numpy: bool = True,
               convert_to_tensor: bool = False,
               device: str = None,
               normalize_embeddings: bool = False) -> Union[List[Tensor], ndarray, Tensor]:
        """
        Computes sentence embeddings
        :param sentences: the sentences to embed
        :param batch_size: the batch size used for the computation
        :param show_progress_bar: Output a progress bar when encode sentences
        :param output_value:  Default sentence_embedding, to get sentence embeddings. Can be set to token_embeddings to get wordpiece token embeddings. Set to None, to get all output values
        :param convert_to_numpy: If true, the output is a list of numpy vectors. Else, it is a list of pytorch tensors.
        :param convert_to_tensor: If true, you get one large tensor as return. Overwrites any setting from convert_to_numpy
        :param device: Which torch.device to use for the computation
        :param normalize_embeddings: If set to true, returned vectors will have length 1. In that case, the faster dot-product (util.dot_score) instead of cosine similarity can be used.
        :return:
           By default, a list of tensors is returned. If convert_to_tensor, a stacked tensor is returned. If convert_to_numpy, a numpy matrix is returned.
        """
        self.eval()
        if show_progress_bar is None:
            show_progress_bar = (logger.getEffectiveLevel()==logging.INFO or logger.getEffectiveLevel()==logging.DEBUG)

        if convert_to_tensor:
            convert_to_numpy = False

        if output_value != 'sentence_embedding':
            convert_to_tensor = False
            convert_to_numpy = False

        input_was_string = False
        if isinstance(sentences, str) or not hasattr(sentences, '__len__'): #Cast an individual sentence to a list with length 1
            sentences = [sentences]
            input_was_string = True

        if device is None:
            device = self._target_device

        self.to(device)

        all_embeddings = []
        length_sorted_idx = np.argsort([-self._text_length(sen) for sen in sentences])
        sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
        maxworklength = 512 # 每次最多提取maxlength个字的特征
        for start_index in trange(0, len(sentences), batch_size, desc="Batches", disable=False):
            # sentences_batch = sentences_sorted[start_index:start_index+batch_size] # sentences_batch里面有batch_size个文本

            tempsentence = sentences_sorted[start_index]
            sentence_length = len(tempsentence)
            if sentence_length%maxworklength:
                numofclip = sentence_length//maxworklength+1
            else:
                numofclip = sentence_length//maxworklength
            if sentence_length:
                features = self.tokenize([tempsentence[clipi*maxworklength:(clipi+1)*maxworklength] for clipi in range(numofclip)])
            else:
                features = self.tokenize([''])
            features = batch_to_device(features, device)

            with torch.no_grad():
                out_features = self.forward(features)

                if output_value == 'token_embeddings':
                    embeddings = []
                    for token_emb, attention in zip(out_features[output_value], out_features['attention_mask']):
                        last_mask_id = len(attention)-1
                        while last_mask_id > 0 and attention[last_mask_id].item() == 0:
                            last_mask_id -= 1

                        embeddings.append(token_emb[0:last_mask_id+1])
                elif output_value is None:  #Return all outputs
                    embeddings = []
                    for sent_idx in range(len(out_features['sentence_embedding'])):
                        row =  {name: out_features[name][sent_idx] for name in out_features}
                        embeddings.append(row)
                else:   #Sentence embeddings
                    embeddings = out_features[output_value]
                    embeddings = embeddings.detach()
                    if normalize_embeddings:
                        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)

                    # fixes for #522 and #487 to avoid oom problems on gpu with large datasets
                    if convert_to_numpy:
                        embeddings = embeddings.cpu() # 维度是[batch_size, 768]
                # all_embeddings.extend(np.average(embeddings, axis=0))
                all_embeddings.append(np.average(embeddings, axis=0).tolist())

        all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)]

        # if convert_to_tensor:
        #     all_embeddings = torch.stack(all_embeddings)
        # elif convert_to_numpy:
        #     all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])

        # if input_was_string:
        #     all_embeddings = all_embeddings[0]

        # ans = np.mean(np.array(all_embeddings), axis=0).tolist()

        return np.array(all_embeddings)

具体的提取特征的代码是调用的sentence_transformers>models>Transformer.py里的tokenize函数。
完成。