围绕特定单词的句子选择

Question

假设我有一段话：

Str_wrds ="Power curve, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs."

并具有以下test_wrds

Test_wrds = ['Power curve', 'data-driven','wind turbines']

每当Test_wrds在段落中找到一个句子时，我想选择它的前后两个句子，并将它们作为一个单独的字符串列出。例如，Test_wrds{}首先出现在第一句话中，因此，当我们选择第二句话时，还有另一个Power curve单词，因此输出类似于

Power curve, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods.

同样，我想将data-driven和wind turbines的句子切分，并将它们保存在单独的字符串中

如何使用Python以简单的方式实现这一点

到目前为止，我发现了一段代码，它基本上可以在任何Text_wrds出现时删除整个句子

def remove_sentence(Str_wrds , Test_wrds):
    return ".".join((sentence for sentence in input.split(".")
                    if Test_wrds not in sentence))

但我不明白如何用这个来解决我的问题

关于这个问题的最新情况：基本上，每当段落中出现test_wrds时，我都会将该句子以及一个句子前后切分，并将其保存在单个字符串中。例如，对于三个text_wrds，我希望得到三个字符串，它们基本上分别覆盖了带有text_wrds的句子。我附加了pdf，例如，输出，我正在寻找

Answer 1

你可以定义一个类似这样的函数

def find_sentences( word, text ):
    sentences = text.split('.')
    findings = []
    for i in range(len(sentences)):
        if word.lower() in sentences[i].lower():
            if i==0:
                findings.append( sentences[i+1]+'.' )
            elif i==len(sentences)-1:
                findings.append( sentences[i-1]+'.' )
            else:
                findings.append( sentences[i-1]+'.' + sentences[i+1]+'.' )
    return findings

这可以称为

findings = find_sentences( 'Power curve', Str_wrds )

用一些漂亮的印刷品

for finding in findings:
print( finding +'\n')

我们得到了结果

However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height.

Power curve, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. Data-driven model accuracy is significantly affected by uncertainty.

The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models.

The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines..

我希望这就是你想要的：）

Answer 2

当你说

I would like to select before and after 1 sentence whenever Test_wrds found it in a paragraph and list them as a separate string.

我猜你的意思是，所有句子中有一个单词在Test_wrds中，在它们前面的句子，在它们后面的句子，也应该被选择

作用

def remove_sentence(Str_wrds: str, Test_wrds):
    # store all selected sentences
    all_selected_sentences = {}
    # initialize empty dictionary
    for k in Test_wrds:
        # one element for each occurrence
        all_selected_sentences[k] = [''] * Str_wrds.lower().count(k.lower())

    # list of sentences
    sentences = Str_wrds.split(".")

    word_counter = {}.fromkeys(Test_wrds,0)

    for i, sentence in enumerate(sentences):
        for j, word in enumerate(Test_wrds):
            # case insensitive
            if word.lower() in sentence.lower():
                if i == 0:  # first sentence
                    chosen_sentences = sentences[0:2]
                elif i == len(sentences) - 1:  # last sentence
                    chosen_sentences = sentences[-2:]
                else:
                    chosen_sentences = sentences[i - 1:i + 2]

                # get which occurrence of the word is it
                k = word_counter[word]

                all_selected_sentences[word][k] += '.'.join(
                    [s for s in chosen_sentences
                        if s not in all_selected_sentences[word][k]]) + "."

                word_counter[word] += 1  # increment the word counter

    return all_selected_sentences

运行这个

answer = remove_sentence(Str_wrds, Test_wrds)
print(answer)

使用为Str_wrds和Test_wrds提供的值， 返回此输出

{
    'Power curve': [
        'Power curve, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height.',
        'Power curve, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty.',
        ' The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models.',
        ' The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs.'
    ],
    'data-driven': [
        ' However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods.',
        ' Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression.',
        ' Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated.'
    ],
    'wind turbines': [
        ' A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs.'
    ]
}

注:

• 函数返回列表
• 每个关键字都是Test_wrds中的一个单词，列表元素是该单词的一个匹配项
• 例如，由于单词“功率曲线”在整个文本中出现4次，因此输出中“功率曲线”的值是4个元素的列表