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152 lines
6.5 KiB
Python
152 lines
6.5 KiB
Python
from char_smi import CharFuncs
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from collections import namedtuple
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from pypinyin import pinyin, Style
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import os
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Correction = namedtuple(
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"Correction",
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[
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"op",
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"toks",
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"inds",
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],
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)
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cache_dir = os.environ.get('COMPASS_DATA_CACHE', '')
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char_smi = CharFuncs(os.path.join(cache_dir, "data", "lawbench", "eval_assets", "char_meta.txt"))
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def check_spell_error(src_span: str,
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tgt_span: str,
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threshold: float = 0.8) -> bool:
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if len(src_span) != len(tgt_span):
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return False
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src_chars = [ch for ch in src_span]
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tgt_chars = [ch for ch in tgt_span]
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if sorted(src_chars) == sorted(tgt_chars): # 词内部字符异位
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return True
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for src_char, tgt_char in zip(src_chars, tgt_chars):
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if src_char != tgt_char:
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if src_char not in char_smi.data or tgt_char not in char_smi.data:
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return False
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v_sim = char_smi.shape_similarity(src_char, tgt_char)
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p_sim = char_smi.pronunciation_similarity(src_char, tgt_char)
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if v_sim + p_sim < threshold and not (
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set(pinyin(src_char, style=Style.NORMAL, heteronym=True)[0]) & set(pinyin(tgt_char, style=Style.NORMAL, heteronym=True)[0])):
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return False
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return True
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class Classifier:
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"""
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错误类型分类器
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"""
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def __init__(self,
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granularity: str = "word"):
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self.granularity = granularity
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@staticmethod
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def get_pos_type(pos):
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if pos in {"n", "nd"}:
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return "NOUN"
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if pos in {"nh", "ni", "nl", "ns", "nt", "nz"}:
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return "NOUN-NE"
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if pos in {"v"}:
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return "VERB"
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if pos in {"a", "b"}:
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return "ADJ"
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if pos in {"c"}:
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return "CONJ"
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if pos in {"r"}:
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return "PRON"
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if pos in {"d"}:
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return "ADV"
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if pos in {"u"}:
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return "AUX"
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# if pos in {"k"}: # TODO 后缀词比例太少，暂且分入其它
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# return "SUFFIX"
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if pos in {"m"}:
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return "NUM"
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if pos in {"p"}:
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return "PREP"
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if pos in {"q"}:
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return "QUAN"
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if pos in {"wp"}:
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return "PUNCT"
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return "OTHER"
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def __call__(self,
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src,
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tgt,
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edits,
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verbose: bool = False):
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"""
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为编辑操作划分错误类型
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:param src: 错误句子信息
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:param tgt: 正确句子信息
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:param edits: 编辑操作
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:param verbose: 是否打印信息
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:return: 划分完错误类型后的编辑操作
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"""
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results = []
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src_tokens = [x[0] for x in src]
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tgt_tokens = [x[0] for x in tgt]
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for edit in edits:
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error_type = edit[0]
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src_span = " ".join(src_tokens[edit[1]: edit[2]])
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tgt_span = " ".join(tgt_tokens[edit[3]: edit[4]])
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# print(tgt_span)
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cor = None
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if error_type[0] == "T":
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cor = Correction("W", tgt_span, (edit[1], edit[2]))
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elif error_type[0] == "D":
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if self.granularity == "word": # 词级别可以细分错误类型
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if edit[2] - edit[1] > 1: # 词组冗余暂时分为OTHER
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cor = Correction("R:OTHER", "-NONE-", (edit[1], edit[2]))
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else:
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pos = self.get_pos_type(src[edit[1]][1])
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pos = "NOUN" if pos == "NOUN-NE" else pos
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pos = "MC" if tgt_span == "[缺失成分]" else pos
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cor = Correction("R:{:s}".format(pos), "-NONE-", (edit[1], edit[2]))
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else: # 字级别可以只需要根据操作划分类型即可
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cor = Correction("R", "-NONE-", (edit[1], edit[2]))
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elif error_type[0] == "I":
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if self.granularity == "word": # 词级别可以细分错误类型
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if edit[4] - edit[3] > 1: # 词组丢失暂时分为OTHER
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cor = Correction("M:OTHER", tgt_span, (edit[1], edit[2]))
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else:
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pos = self.get_pos_type(tgt[edit[3]][1])
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pos = "NOUN" if pos == "NOUN-NE" else pos
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pos = "MC" if tgt_span == "[缺失成分]" else pos
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cor = Correction("M:{:s}".format(pos), tgt_span, (edit[1], edit[2]))
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else: # 字级别可以只需要根据操作划分类型即可
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cor = Correction("M", tgt_span, (edit[1], edit[2]))
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elif error_type[0] == "S":
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if self.granularity == "word": # 词级别可以细分错误类型
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if check_spell_error(src_span.replace(" ", ""), tgt_span.replace(" ", "")):
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cor = Correction("S:SPELL", tgt_span, (edit[1], edit[2]))
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# Todo 暂且不单独区分命名实体拼写错误
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# if edit[4] - edit[3] > 1:
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# cor = Correction("S:SPELL:COMMON", tgt_span, (edit[1], edit[2]))
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# else:
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# pos = self.get_pos_type(tgt[edit[3]][1])
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# if pos == "NOUN-NE": # 命名实体拼写有误
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# cor = Correction("S:SPELL:NE", tgt_span, (edit[1], edit[2]))
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# else: # 普通词语拼写有误
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# cor = Correction("S:SPELL:COMMON", tgt_span, (edit[1], edit[2]))
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else:
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if edit[4] - edit[3] > 1: # 词组被替换暂时分为OTHER
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cor = Correction("S:OTHER", tgt_span, (edit[1], edit[2]))
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else:
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pos = self.get_pos_type(tgt[edit[3]][1])
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pos = "NOUN" if pos == "NOUN-NE" else pos
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pos = "MC" if tgt_span == "[缺失成分]" else pos
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cor = Correction("S:{:s}".format(pos), tgt_span, (edit[1], edit[2]))
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else: # 字级别可以只需要根据操作划分类型即可
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cor = Correction("S", tgt_span, (edit[1], edit[2]))
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results.append(cor)
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if verbose:
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print("========== Corrections ==========")
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for cor in results:
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print("Type: {:s}, Position: {:d} -> {:d}, Target: {:s}".format(cor.op, cor.inds[0], cor.inds[1], cor.toks))
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return results
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# print(pinyin("朝", style=Style.NORMAL))
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