- Name three machine learning libraries able to classify short texts using supervised learning
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Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides a range of classification algorithms for short text classification, such as Naive Bayes, Support Vector Machines (SVM), and Random Forests.
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Keras: Keras is a high-level neural network library in Python that provides a simple and easy-to-use interface for building and training deep learning models. It includes a range of pre-built models and layers that can be used for text classification tasks.
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TensorFlow: TensorFlow is an open-source machine learning library in Python developed by Google that includes a range of tools and APIs for building and training deep learning models. It can be used for short text classification tasks using a range of neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- Name three Java machine learning libraries able to classify short texts using supervised learning
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Weka: Weka is a popular open-source machine learning library in Java that provides a range of classification algorithms for short text classification, such as Naive Bayes, Support Vector Machines (SVM), and Decision Trees.
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Stanford CoreNLP: Stanford CoreNLP is a natural language processing library in Java that provides a range of tools for text analysis, including named entity recognition, sentiment analysis, and text classification using machine learning algorithms.
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Apache Mahout: Apache Mahout is an open-source machine learning library in Java that provides a range of algorithms and tools for clustering, classification, and recommendation tasks. It includes a range of algorithms for text classification tasks, such as Naive Bayes and Random Forests.
- Compare the use of decision trees with other methods for classifying short texts
Decision trees are a popular and effective method for classifying short texts, but they have both advantages and disadvantages compared to other methods.
Advantages:
Decision trees are relatively simple to implement and interpret, making them a good choice for beginners or those with limited programming experience. Decision trees can handle both categorical and numerical data, making them versatile for a wide range of classification tasks. Decision trees can easily handle missing or noisy data, which can be common in short text classification tasks. Decision trees can be combined with other methods, such as ensemble methods or boosting, to improve their accuracy and performance.
Disadvantages:
Decision trees can suffer from overfitting, where they fit the training data too closely and perform poorly on new data. This can be mitigated by pruning or ensemble methods, but it still requires careful tuning and validation. Decision trees can be biased towards features with many categories or high cardinality, which can affect their accuracy and performance. Decision trees can be sensitive to changes in the data, which can lead to instability in the model and require frequent retraining. Compared to other methods, such as support vector machines (SVMs), deep learning, or ensemble methods, decision trees may not always achieve the highest accuracy or performance in short text classification tasks. However, they are still a useful and effective method that can provide a simple and interpretable solution for many classification tasks.
Must read:
Merlini & Rossini, "Text categorization with WEKA: A survey" https://www.sciencedirect.com/science/article/pii/S2666827021000141
What we did in the end can be read in: https://zenodo.org/record/8199859