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