Awesome

POS Tagger

Build a POS Tagger for Lao and Bahasa Indonesia
2020云山杯“低资源语言序列标注任务评测”任务：印尼语和老挝语词性标注
..云山
<img src='https://upload.thwiki.cc/thumb/a/af/%E4%BA%91%E5%B1%85%E4%B8%80%E8%BD%AE%26%E4%BA%91%E5%B1%B1%EF%BC%88%E6%B1%82%E9%97%BB%E5%8F%A3%E6%8E%88%EF%BC%89.png/450px-%E4%BA%91%E5%B1%85%E4%B8%80%E8%BD%AE%26%E4%BA%91%E5%B1%B1%EF%BC%88%E6%B1%82%E9%97%BB%E5%8F%A3%E6%8E%88%EF%BC%89.png' height="300px">

版本说明

• kashigari v2.0.1 (需要 tensorflow>=2.2.0，2.4.0以上会有warning，目前不影响运行)

结构说明

• corpus 语料文件夹
• reference 词性对照表等 参考资料
• output 模型输出
• preprocess 语料预处理

• dataset_split.py 挑选训练/验证/测试集
• format_process.py 调整语料格式
• process_test.py 测试集的预处理

• utils.py 工具包(与模型训练无直接关联的工作)
• run.py 模型训练

框架修改

基于kashgari框架进行了一些修改，以便于适配词性识别

• 更改 .../kashgari/metrics/sequence_labeling.py 中的get_entities()函数如下

def get_entities(seq: List[str], *, suffix: bool = False) -> List[Tuple[str, int, int]]:
    """Gets entities from sequence.
    Args:
        seq: sequence of labels.
        suffix:
    Returns:
        list: list of (chunk_type, chunk_start, chunk_end).
    Example:
        >>> from kashgari.metrics.sequence_labeling import get_entities
        >>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']
        >>> get_entities(seq)
        [('PER', 0, 1), ('LOC', 3, 3)]
    """
    prev_tag = 'O'
    prev_type = ''
    begin_offset = 0
    chunks = []
    for i, chunk in enumerate(seq + ['O']):
        # if suffix:
        #     tag = chunk[-1]
        #     type_ = chunk.split('-')[0]
        # else:
        #     tag = chunk[0]
        #     type_ = chunk.split('-')[-1]
        #
        # if end_of_chunk(prev_tag, tag, prev_type, type_):
        #     chunks.append((prev_type, begin_offset, i - 1))
        # if start_of_chunk(prev_tag, tag, prev_type, type_):
        #     begin_offset = i
        # prev_tag = tag
        # prev_type = type_
        chunks.append((chunk, i-1, i-1))

    return chunks

结果 排名(队伍：DUFLER)

算法说明 BiLSTM_CRF

总结：只能说很幸运😂