Introduction to NLP: Supervised Classification

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Questions and Answers

What is the primary goal of text classification?

Identifying the emotional tone of a piece of text.

Translating text into another language automatically.

Assigning a predefined category or label to a given text. (correct)

Determining the author of a given text.

Which of the following is NOT mentioned as an example of a classification task?

Categorizing a news article by its topic.

Detecting whether an email is spam.

Identifying the language in a text document. (correct)

Determining the sense of the word 'bank' in a sentence.

What is a 'supervised classifier'?

A classifier that relies exclusively on pre-programmed rules.

A classifier that learns from training data with labeled examples. (correct)

A classifier that requires manual adjustments during runtime.

A classifier which is not reliable.

When building a text classifier, what is the first step after deciding on the task?

Deciding which features of the input are relevant. Signup and view all the answers

In the gender identification example, what is a 'feature set'?

A dictionary mapping features' names to their values. Signup and view all the answers

What is the primary purpose of calculating the accuracy of a classifier on a test set?

To evaluate the classifier's ability to generalize to unseen data. Signup and view all the answers

In the gender identification task, if only the final letters are analyzed, which name would have a HIGHER probability of being classified as 'male'?

Kate Signup and view all the answers

After the data is processed with the feature extractor function, how is it typically divided before training a classifier?

Into a training set and a test set. Signup and view all the answers

In the context of part-of-speech tagging, what advantage does a trained classifier offer over a handcrafted regular expression tagger?

A classifier learns informative patterns from data, rather than relying solely on prior rules. Signup and view all the answers

Which classifier is used in the content for learning to classify text?

A naive Bayes classifier. Signup and view all the answers

What is the role of a feature extraction function in the part-of-speech tagging process?

To highlight specific characteristics of words for the classifier to use. Signup and view all the answers

Why might a decision tree classifier start by checking if a word ends with a comma?

Because it's a simple and very common tag. Signup and view all the answers

What is a consequence of using a feature extraction function?

It limits the classifier's view to the highlighted features, possibly missing other relevant properties. Signup and view all the answers

If a word ends in 's', what is the most likely tag it would receive in the example provided by the text?

Verb (VBZ) Signup and view all the answers

What is a potential way the provided part-of-speech tagger could be modified to utilize more word information?

By adding word internal feature, such as word length or number of syllables. Signup and view all the answers

How can the decision tree model be presented so that it can be understood and interpreted more easily?

As a series of if else statements Signup and view all the answers

When tagging the word 'fly', what contextual information is most helpful in determining its part of speech?

The word that immediately precedes 'fly'. Signup and view all the answers

When adapting a feature extractor to consider context, what needs to be passed into the revised pattern?

A complete untagged sentence and the index of the target word. Signup and view all the answers

Why is it crucial for the test set to be separate from the training set during model evaluation?

To ensure the model can generalize to new, unseen data. Signup and view all the answers

If a model is evaluated using the same data it was trained on, what risk is most likely?

The model will receive an artificially high score by remembering the training data. Signup and view all the answers

What is a key trade-off to consider when creating test sets?

The trade-off between the amount of data for testing and for training. Signup and view all the answers

For a typical POS tagging task with a small amount of well-balanced labels and a diverse range of data, how small can a test set be for meaningful evaluation?

As low as 100 evaluation instances. Signup and view all the answers

What is a primary concern when a training set and a test set are derived from the same genre?

The evaluation results might generalize poorly to other genres. Signup and view all the answers

For classification tasks with a large number of labels or infrequent labels, what should determine the size of the test set?

The size should ensure that the least frequent label appears at least 50 times. Signup and view all the answers

How does using `random.shuffle()` affect the test set in relation to the training set?

It can lead to the test set containing sentences from the same documents used for training. Signup and view all the answers

What happens if the test set is created from sentences randomly assigned from the same genre as the training set?

The test set will be very similar to the training set. Signup and view all the answers

What is a potential consequence of having similar patterns or specific word frequencies within a document used for both training and testing?

It causes the test set to reflect biases in the training data. Signup and view all the answers

What is a more robust approach to constructing training and test sets, as compared to sampling from the same documents?

Ensuring the training set and test set are drawn from different documents. Signup and view all the answers

If a model performs well on a test set from documents less closely related to the training set, what can be inferred?

The model has the ability to generalize beyond the specific training set. Signup and view all the answers

A name gender classifier predicts correctly 60 out of 80 names, what is its accuracy?

75% Signup and view all the answers

Why should the class label frequencies in the test set be evaluated before interpreting the accuracy scores?

Because a high accuracy may be misleading if there are imbalanced class frequencies. Signup and view all the answers

In the context of search tasks like information retrieval, what can make accuracy scores misleading?

When the majority of documents are not relevant. Signup and view all the answers

Which metric is defined as the proportion of correctly identified relevant items among all items identified as relevant?

Precision Signup and view all the answers

A model identifies 70 relevant documents, 60 of which are actually relevant. What is the precision of this model?

$60/70$ Signup and view all the answers

If a model has a precision of 0.8 and a recall of 0.6, what is the F-measure?

0.667 Signup and view all the answers

Which of the following describes a Type II error?

Classifying a relevant item as irrelevant Signup and view all the answers

In a confusion matrix, what do the off-diagonal entries typically represent?

Errors made by the model Signup and view all the answers

What is cross-validation primarily intended to address?

Mitigating the impact of small test sets Signup and view all the answers

In k-fold cross-validation, how many times is the model trained?

k times, each time using a different fold as a test set Signup and view all the answers

If a model labels every document as irrelevant, why would the accuracy score be misleadingly high?

Because the number of irrelevant documents is far higher than the relevant ones Signup and view all the answers

What is the primary purpose of using `nltk.classify.apply_features` when working with large datasets?

To generate an object that behaves like a list without storing all feature sets in memory. Signup and view all the answers

What is the 'kitchen sink' approach in feature selection?

Including all possible features at the beginning for assessment. Signup and view all the answers

What is a likely consequence of using too many features in a learning algorithm?

Increased the likelihood of overfitting and poor generalization on new data. Signup and view all the answers

What is the key purpose of the dev-test set in error analysis when developing a model?

To identify the errors made by the classifier to refine the feature set. Signup and view all the answers

What does the term 'likelihood ratio' indicate when analyzing features related to gender identification?

The ratio of the probabilities of a given name ending in a specific letter for each gender. Signup and view all the answers

What is the last step used to evaluate the system after error analysis using the dev-test subset?

Evaluate the final model using a separate test set. Signup and view all the answers

What is the concept of 'overfitting' in the context of training a classifier?

The classifier relies too much on its training data and is poor on new ones. Signup and view all the answers

When dividing the corpus data for model development, what are the roles of training, dev-test, and test sets?

The training set is to train the model, the dev-test is for error analysis/feature refinement, and the test set is the final evaluation. Signup and view all the answers

Study Notes

Introduction to Natural Language Processing: Learning to Classify Text

The goal of this chapter is to answer two questions:
- How can we identify features of language data that are important for classification?
- How can we construct language models to automatically perform language processing tasks?

Supervised Classification

Classification is choosing the correct class label for a given input.
Examples of classification tasks:
- Determining if an email is spam or not.
- Identifying the topic of a news article (e.g., sports, technology, politics).
- Determining if the word "bank" refers to a river bank, a financial institution, or an action.

Supervised Classification Framework

A supervised classifier uses training corpora with correct labels for each input.
The framework involves:
- Training: Input data with labels → Feature extractor extracts features → Machine learning algorithm transforms these features into a classifier model
- Prediction: Input feature sets → Classifier model → Predicted label

Gender Identification

Names ending in 'a', 'e', and 'i' are often female.
Names ending in 'k', 'o', 'r', 's', and 't' are often male.
Feature extraction function: Extracts the last letter of a name and returns a dictionary {'last_letter': 'letter'}.
Example: gender_features('Shrek') returns {'last_letter': 'k'}.

Choosing the Right Features

Feature selection significantly impacts the model
Start with a "kitchen sink" approach, including all possible features.
Use an error analysis procedure to refine the feature set
This avoids overfitting on the training data
Evaluate the model on a development set, subdivided into training and dev-test set

Document Classification

Using corpora (e.g., Movie Reviews), we create labeled document lists
Example: Movie reviews are classified as positive or negative

Document Feature Extraction

Create a list of frequent words (e.g., top 2,000).
Define a feature extractor to check for words' presence, e.g. document_features('document'), returning True or False.
Example: Features include indicators for if the word is present, e.g., 'contains(plot)'

Part-of-Speech Tagging

A feature extractor identifies suffixes for classifying parts of speech
Examples of features: 'endswith(,)', 'endswith(the)'
NLTK can generate pseudocode for decision trees to visualize the decision-making in classification tasks

Exploiting Context

Contextual features like previous words are crucial.
A revised feature extractor considers the complete sentence and the target word's position.

Evaluation

Evaluation determines if the model accurately captures patterns in text
Key metrics: Accuracy, Precision, and Recall, and the F-measure.
A Confusion Matrix visualizes classification errors, especially for models with multiple labels

The Test Set

The test set should be distinct from the training set
The testing set should be diverse and large enough to reflect the real-world instances accurately
Size considerations for different tasks, especially for tasks having a small number of well balanced labels and diverse test set.

Cross-Validation

A way to evaluate models by performing multiple evaluations on different test sets, which combines scores for reliable evaluation on the combined datasets

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Description

This quiz focuses on the foundations of natural language processing, particularly in the context of supervised classification. It explores the identification of important language features and the construction of models to automate classification tasks. Test your understanding of how classification works for various applications, including email spam detection and topic identification.