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Questions and Answers
What is the main purpose of using coherence measures in topic modeling?
What is the main purpose of using coherence measures in topic modeling?
To judge the relative quality of a fit
Why is the weighted geometric average not intuitively interpretable as probability?
Why is the weighted geometric average not intuitively interpretable as probability?
Weighted geometric average is not intuitively interpretable as probability.
Explain the basic idea behind coherence measures in topic modeling.
Explain the basic idea behind coherence measures in topic modeling.
In a coherent topic, the top words cooccur in the same documents.
What is the purpose of using a reference corpus like Wikipedia in coherence computation?
What is the purpose of using a reference corpus like Wikipedia in coherence computation?
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Why is topic model evaluation considered difficult?
Why is topic model evaluation considered difficult?
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What is the purpose of manual inspection of the most important words in each topic in topic modeling?
What is the purpose of manual inspection of the most important words in each topic in topic modeling?
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How is the evaluation of topic modeling often done in relation to perplexity and coherence?
How is the evaluation of topic modeling often done in relation to perplexity and coherence?
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Explain the word intrusion task in the context of topic modeling evaluation.
Explain the word intrusion task in the context of topic modeling evaluation.
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What are some caveats to using the likelihood of observing training data as a quality measure in probabilistic topic models?
What are some caveats to using the likelihood of observing training data as a quality measure in probabilistic topic models?
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What is the relationship between topic modeling and cluster analysis?
What is the relationship between topic modeling and cluster analysis?
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What is the entropy of a sample and how is it related to cluster evaluation?
What is the entropy of a sample and how is it related to cluster evaluation?
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Why is evaluation a major challenge in topic modeling?
Why is evaluation a major challenge in topic modeling?
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How does cross-entropy in probability relate to comparing two probability distributions?
How does cross-entropy in probability relate to comparing two probability distributions?
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What does the Kullback-Leibler divergence measure in probability distributions?
What does the Kullback-Leibler divergence measure in probability distributions?
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How does the interpretability of topic models relate to the ability to explain held out documents with existing clusters?
How does the interpretability of topic models relate to the ability to explain held out documents with existing clusters?
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What is the geometric average in probability and how does it relate to encoding data?
What is the geometric average in probability and how does it relate to encoding data?
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Why is it noted that in cross-entropy, we do not know the true probabilities?
Why is it noted that in cross-entropy, we do not know the true probabilities?
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Study Notes
Weighted Geometric Average
- Not suitable for interpreting probability
- Has been used to judge the relative quality of a fit
Coherence
- Measures the quality of a topic model
- Based on the idea that in a coherent topic, the top words co-occur in the same documents
- Computed within the original corpus or a reference corpus such as Wikipedia
- Several variants of this measure have been discussed in literature
Evaluation of Topic Models
- Difficult to evaluate due to disconnect between how topic models are evaluated and why they are expected to be useful
- Evaluation methods include:
- Manual inspection of top words in each topic
- Perplexity and coherence
- Secondary task evaluation (e.g., classification, IR)
- Ability to explain held-out documents with existing clusters
- Word intrusion task
- Topic intrusion task
Relation to Cluster Analysis
- Topics are comparable to cluster centers
- Documents may belong to multiple topics
- Algorithms share ideas, such as EM
- Algorithms contain special adaptations for text (e.g., sparsity, priors)
- Computationally expensive and scalable
- Evaluation is a major challenge, similar to clustering
- Subjective quality does not always agree with quality measures
Evaluation of Probabilistic Topic Models
- Compute probabilities for observing documents
- Training involves maximizing the likelihood of observing training data
- Challenges in using likelihood as a quality measure:
- Overfitting
- Model complexity
- Comparability issues
- Computational problems when approximating reference probabilities
Shannon Entropy
- Measures the minimum average number of bits required to encode values of an infinite sample
- Intuition: number of bits of "information" in each object
- Examples:
- Fair coin toss: 1 bit
- Fair dice: 3.32 bits
- Two dice: 5.64 bits
- Sum of two dice: 3.32 bits
- Uniform 2...12: 3.58 bits
Cross-Entropy
- Compares two probability distributions
- Intuition: encode data distributed as Q with the encoding scheme obtained by P
- Related to Kullback-Leibler divergence, which is the excess entropy
- Does not require knowledge of true probabilities
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Description
Explore different evaluation methods used in topic modeling, such as manual inspection of important words, perplexity, coherence, secondary task evaluation, and the word intrusion task. Understand how to assess the ability of topic models to explain held out documents and identify intruder words.
