nice sentences

本文精选了关于生活哲理的名言,提醒我们珍惜当下,微笑面对人生,即使在困难中也能感受到生活的美好。

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1.Don't cry because it is over ,smile because it happened!

2.To the world you may be one person ,but to one person you may be the world.

3.No matter the ending is perfect or not ,you can't disappear from my world.

4.I know i am not strong but i pretend to strong because you are not around.

5.The best and most beautiful things in the world cannot be seen or even touched. they must be felt with the heart.

6.Yesterday is past ,it is history. Tomorrow is future ,it is mystery .Today is present ,it is a gift.

7.when you leave me ,please don't comfort me ,because each sewing has to meet stinging pain.

8.My heart hurts so much to the point of death,But I'm still worried about you.

转载于:https://www.cnblogs.com/qingjoin/archive/2011/12/28/2304538.html

### 使用Python进行文本问题提取的方法和库 #### 方法一:基于正则表达式的匹配 对于结构化程度较高的文本,可以利用`re`模块来进行模式匹配。例如,在给定的一段文字里查找特定格式的问题描述。 ```python import re def extract_questions_by_regex(text): pattern = r'(\d+\.\s*(.*?)(?= \d+\.|\Z))' matches = re.findall(pattern, text, flags=re.DOTALL) questions = [] for match in matches: question_number_and_content = match[0].strip() questions.append(question_number_and_content) return questions text_with_numbers = """1. What is your name? My name is John. 2. Where do you live? I live on Earth.""" questions_extracted = extract_questions_by_regex(text_with_numbers) for q in questions_extracted: print(q) ``` 这种方法适用于具有明显编号或其他可识别特征的问题列表[^2]。 #### 方法二:自然语言处理技术 当面对非结构化的纯文本时,则可能需要用到更加复杂的NLP工具来解析句子并识别其中的问题句型。SpaCy是一个强大的开源NLP框架,能够帮助完成这项工作。 安装spaCy及相关模型: ```bash pip install spacy python -m spacy download en_core_web_sm ``` 编写代码实现问题检测功能: ```python import spacy nlp = spacy.load("en_core_web_sm") def detect_question(sentence): doc = nlp(sentence) if any(token.tag_.startswith('W') or token.lower_ == 'how' for token in doc): # W代表疑问词标签 return True elif sentence.strip().endswith('?'): return True else: return False sample_text = "Can you tell me what time it is now?" if detect_question(sample_text): print(f"'{sample_text}' 是一个问题.") else: print(f"'{sample_text}' 不是问题.") unstructured_paragraph = """ The weather today seems quite nice. How can I make the most out of this sunny day? Also, could someone remind me when our next meeting will be held again? """ sentences = unstructured_paragraph.split('.') question_sentences = [sentence.strip()+'.' for sentence in sentences if detect_question(sentence)] print("\n".join(question_sentences)) ``` 此方案不仅限于简单的问号结尾判断,还考虑到了英语中常见的疑问代词开头的情况,从而提高了准确性[^4]。 #### 方法三:使用预训练的语言模型 Hugging Face Transformers提供了许多已经过大规模语料库训练过的深度学习模型,可以直接用于各种下游任务,包括但不限于问答系统构建。BERT(Bidirectional Encoder Representations from Transformers)就是这样一个优秀的双向编码器表示模型家族成员之一。 首先确保已安装必要的软件包: ```bash pip install transformers torch ``` 接着定义一个简单函数来调用transformers库中的pipeline接口执行零样本分类任务,以此作为初步筛选手段找出潜在的问题表述: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") def classify_as_question(text): candidate_labels = ["statement", "question"] result = classifier(text, candidate_labels=candidate_labels) label = result['labels'][result['scores'].index(max(result['scores']))] return label == "question" test_string = "Is there life after death?" is_it_a_question = classify_as_question(test_string) print(is_it_a_question) ``` 上述三种方式各有优劣,可以根据具体的业务场景灵活选用最合适的策略组合[^3]。
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