nlp--最大匹配分词(计算召回率)

最大匹配算法是一种常见的中文分词算法，其核心思想是从左向右取词，以词典中最长的词为优先匹配。这里我将为你展示一个简单的最大匹配分词算法的实现，并结合输入任意句子、显示分词结果以及计算分词召回率。

代码 :

# happy coding
# -*- coding: UTF-8 -*-
'''
@project:NLP
@auth:y1441206
@file:最大匹配法分词.py
@date:2024-06-30 16:08
'''
class MaxMatchSegmenter:def __init__(self, dictionary):self.dictionary = dictionaryself.max_length = max(len(word) for word in dictionary)def segment(self, text):result = []index = 0n = len(text)while index < n:matched = Falsefor length in range(self.max_length, 0, -1):if index + length <= n:word = text[index:index+length]if word in self.dictionary:result.append(word)index += lengthmatched = Truebreakif not matched:result.append(text[index])index += 1return resultdef calculate_recall(reference, segmented):total_words = len(reference)correctly_segmented = sum(1 for word in segmented if word in reference)recall = correctly_segmented / total_words if total_words > 0 else 0return recall# Example usage
if __name__ == "__main__":# Example dictionarydictionary = {"北京", "天安门", "广场", "国家", "博物馆", "人民", "大会堂", "长城"}# Example text to segmenttext = "北京天安门广场是中国的象征，国家博物馆和人民大会堂也在附近。"# Initialize segmenter with dictionarysegmenter = MaxMatchSegmenter(dictionary)# Segment the textsegmented_text = segmenter.segment(text)# Print segmented resultprint("分词结果:", " / ".join(segmented_text))# Example for calculating recallreference_segmentation = ["北京", "天安门广场", "是", "中国", "的", "象征", "，", "国家", "博物馆", "和", "人民大会堂", "也", "在", "附近", "。"]recall = calculate_recall(reference_segmentation, segmented_text)print("分词召回率:", recall)

运行结果 :