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- #!/usr/bin/env python
- import re
- import numpy as np
- def forward_segment(text, seg_dict):
- word_list = []
- i = 0
- while i < len(text):
- longest_word = text[i]
- for j in range(i + 1, len(text) + 1):
- word = text[i:j]
- if word in seg_dict:
- if len(word) > len(longest_word):
- longest_word = word
- word_list.append(longest_word)
- i += len(longest_word)
- return word_list
- def seg_tokenize(txt, seg_dict):
- out_txt = ""
- pattern = re.compile(r"([\u4E00-\u9FA5A-Za-z0-9])")
- for word in txt:
- if pattern.match(word):
- if word in seg_dict:
- out_txt += seg_dict[word] + " "
- else:
- out_txt += "<unk>" + " "
- else:
- continue
- return out_txt.strip().split()
- def tokenize(data,
- vocab=None,
- seg_dict=None):
- assert "text" in data
- assert isinstance(vocab, dict)
- text = data["text"]
- token = []
- if seg_dict is not None:
- assert isinstance(seg_dict, dict)
- txt = forward_segment("".join(text).lower(), seg_dict)
- text = seg_tokenize(txt, seg_dict)
-
- for x in text:
- if x in vocab:
- token.append(vocab[x])
- else:
- token.append(vocab['<unk>'])
- data["text"] = np.array(token)
- return data
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