Sentence selection surrounded to a particular words

Question

Suppose I have a paragraph:

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."

And have the following test_wrds,

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

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. For example, Test_wrds Power curve appeared first in 1st sentence hence but when we select 2nd sentence there are another Power curve words thus the output would be something like this

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.

And likewise, I would like to slice sentences for data-driven and wind turbines and saved them in separate strings.

How can I implement this using Python in a simple way?

So far I found code which basically removes the entire sentence whenever any Text_wrds is in.

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

But I don't understand how to use this for my problem.

update on the problem: Basically, whenever there is test_wrds present in the paragraph, I would like to slice that sentence as well as before and after one sentence and saved it on a single string. So for example for three text_wrds I am expected to get three strings which basically covers sentences with text_wrds individually. I attached pdf, for example, the output, I am looking for

Answer 1

You could define a function something like this one

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

This can then be called as

findings = find_sentences( 'Power curve', Str_wrds )

With some pretty printing

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

We get the results

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..

which I hope is what you where looking for :)

Answer 2

When you say,

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.

I guess you mean that, all the sentences that have one of the words in Test_wrds in them, the sentence before them, and after them, should also be selected.

Function

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

Running this

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

with the provided values for Str_wrds and Test_wrds, returns this output

{
    '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.'
    ]
}

Notes:

• the function returns a dict of lists
• every key is a word in Test_wrds, and list element is an occurrence of the word.
• for example, because the word 'power curve' occurs 4 times in the entire text, the value for 'power curve' in the output is a list of 4 elements.