Best way to extract keywords from input NLP sentence

Question

I'm dealing with a venture where I need to extract significant keywords from a sentence. I've been utilizing a principle put together framework based with respect to the POS tags. Nonetheless, I run into some equivocal terms that I've been not able to parse. Is there some ML classifier that I can use to extract pertinent keywords dependent on a preparation set of various sentences?

Answer 1

Yes, you can use machine learning classifiers to extract relevant keywords from sentences based on a training set of labeled data. One common approach for keyword extraction is to treat it as a supervised learning problem, where you train a classifier using labeled examples of sentences and their corresponding keywords.

Here's a general workflow for keyword extraction using machine learning:

Collect and prepare a training dataset: Gather a set of sentences and label them with the relevant keywords. This dataset will be used to train the classifier.

Feature extraction: Transform the sentences into a suitable numerical representation that captures their relevant characteristics. This step involves extracting features such as word frequencies, POS tags, n-grams, or any other relevant information from the sentences.

Split the dataset: Divide the labeled dataset into training and testing subsets. The training subset will be used to train the classifier, while the testing subset will evaluate its performance.

Train a classifier: Select a machine learning algorithm suitable for text classification, such as Naive Bayes, Support Vector Machines (SVM), or Random Forests. Train the classifier using the labeled training dataset and the extracted features.

Evaluate the classifier: Use the labeled testing dataset to assess the performance of the trained classifier. Measure metrics such as precision, recall, and F1-score to evaluate how well it predicts the relevant keywords.

Apply the classifier: Once the classifier is trained and evaluated, you can use it to predict keywords for new, unseen sentences. Extract the relevant features from the sentences and feed them into the classifier to obtain the predicted keywords.

Answer 2

You can use multilingual Rake package. It can be installed with this below line:

pip install multi-rake

Check the below code:

from multi_rake import Rake

text_en = (

    'Compatibility of systems of linear constraints over the set of '

    'natural numbers. Criteria of compatibility of a system of linear '

    'Diophantine equations, strict inequations, and nonstrict inequations '

    'are considered. Upper bounds for components of a minimal set of '

    'solutions and algorithms of construction of minimal generating sets '

    'of solutions for all types of systems are given. These criteria and '

    'the corresponding algorithms for constructing a minimal supporting '

    'set of solutions can be used in solving all the considered types of '

    'systems and systems of mixed types.'

)

rake = Rake()

keywords = rake.apply(text_en)

print(keywords[:10])

#  ('minimal generating sets', 8.666666666666666),

#  ('linear diophantine equations', 8.5),

#  ('minimal supporting set', 7.666666666666666),

#  ('minimal set', 4.666666666666666),

#  ('linear constraints', 4.5),

#  ('natural numbers', 4.0),

#  ('strict inequations', 4.0),

#  ('nonstrict inequations', 4.0),

#  ('upper bounds', 4.0),

#  ('mixed types', 3.666666666666667)

Are you looking for a good python tutorial? Join the python course fast and gain more knowledge in python.

Answer 3

! In your project, where the goal is to extract meaningful keywords from sentences, you have been using a rule-based system based on POS tags. However, you have encountered difficulties in parsing ambiguous terms, which have hindered the accuracy of your keyword extraction. To overcome this challenge, you can leverage machine learning classifiers.

The first step is to collect a training dataset consisting of labeled examples, where sentences are paired with their corresponding keywords. This dataset will serve as the basis for training the classifier. Once you have the dataset, the next step is feature extraction. You need to transform the sentences into a numerical representation that captures their relevant characteristics. This process involves extracting features such as word frequencies, POS tags, n-grams, or any other relevant information that can contribute to keyword identification.

After feature extraction, you need to split the dataset into training and testing subsets. The training subset is used to train the machine learning classifier, while the testing subset is used to evaluate its performance. Select an appropriate algorithm for text classification, such as Naive Bayes, Support Vector Machines (SVM), or Random Forests. Train the classifier using the labeled training dataset and the extracted features.

Once the classifier is trained, it's time to evaluate its performance. Use the labeled testing dataset to assess how well the classifier predicts the relevant keywords. Metrics such as precision, recall, and F1-score can be used to measure the accuracy and effectiveness of the classifier.

With a trained and evaluated classifier in hand, you can apply it to new, unseen sentences to predict the relevant keywords. Extract the necessary features from the sentences and input them into the classifier to obtain the predicted keywords.

It's important to note that this approach requires a labeled training dataset, which means manually creating or sourcing one where each sentence is associated with its relevant keywords. This can be done through manual labeling or by leveraging existing labeled datasets or crowdsourcing.

By incorporating machine learning classifiers into your keyword extraction process, you can enhance accuracy and handle ambiguous terms more effectively. This approach allows for a more robust and automated extraction of meaningful keywords from sentences, aiding in the overall success of your project.

Answer 4

To extract keywords from sentences, you can use machine learning classifiers. Collect a labeled training dataset with sentence-keyword pairs. Extract features from the sentences, split the dataset into training and testing subsets, and train a classifier using the chosen algorithm. Evaluate its performance using the testing subset. Once trained, apply the classifier to new sentences to predict keywords. Remember to create or source a labeled training dataset. Machine learning classifiers improve keyword extraction by handling ambiguity and increasing accuracy.