Virtual Labs

Building POS Tagger

After completing the simulation, which statement best describes the relationship between training corpus size and POS tagging accuracy?

a: Accuracy always increases linearly with corpus size Explanation

Explanation

b: Accuracy improves with larger corpora but shows diminishing returns Explanation

Explanation

c: Corpus size has no impact on accuracy Explanation

Explanation

d: Smaller corpora always produce better results Explanation

Explanation

Based on your experimental observations, which context feature configuration typically provides the best accuracy?

a: Unigram features only Explanation

Explanation

b: Bigram features Explanation

Explanation

c: Trigram features Explanation

Explanation

d: All configurations perform equally Explanation

Explanation

From your simulation experience, which algorithm generally performed better for POS tagging?

a: Hidden Markov Model (HMM) always outperforms CRF Explanation

Explanation

b: Conditional Random Field (CRF) typically achieves higher accuracy Explanation

Explanation

c: Both algorithms perform identically Explanation

Explanation

d: Performance depends only on corpus size, not algorithm choice Explanation

Explanation

When you tested different language options (English vs Hindi), what key difference did you likely observe?

a: Both languages showed identical accuracy patterns Explanation

Explanation

b: Different languages may show varying accuracy due to linguistic complexity Explanation

Explanation

c: Hindi always performs better than English Explanation

Explanation

d: English always performs better than Hindi Explanation

Explanation

Based on your experiments, what is the primary advantage of using CRF over HMM for POS tagging?

a: CRF is always faster to train and execute Explanation

Explanation

b: CRF can incorporate a wider variety of features and dependencies Explanation

Explanation

c: CRF requires less training data Explanation

Explanation

d: CRF works only with large vocabularies Explanation

Explanation

In your simulation experiments, which configuration would you choose for a real-world application requiring high accuracy?

a: HMM with unigram features and small corpus Explanation

Explanation

b: CRF with trigram features and large corpus Explanation

Explanation

c: HMM with trigram features and medium corpus Explanation

Explanation

d: Any configuration since they all perform equally Explanation

Explanation

From the demo examples you explored, why might the word 'can' be challenging for POS taggers?

a: It's a very rare word Explanation

Explanation

b: It can function as both a noun and a verb depending on context Explanation

Explanation

c: It only appears in technical documents Explanation

Explanation

d: It's not in English dictionaries Explanation

Explanation

After experimenting with the simulation, what would be the most effective strategy to improve POS tagging accuracy for a low-resource language?

a: Use only unigram features to avoid overfitting Explanation

Explanation

b: Combine transfer learning from high-resource languages with available data Explanation

Explanation

c: Always use the smallest possible training corpus Explanation

Explanation

d: Ignore contextual features completely Explanation

Explanation

Based on your experimental observations, when might you choose HMM over CRF despite CRF's generally higher accuracy?

a: When computational resources are severely limited Explanation

Explanation

b: When you need the highest possible accuracy Explanation

Explanation

c: When working with large training corpora Explanation

Explanation

d: Never, CRF is always the better choice Explanation

Explanation

Reflecting on your complete experimental experience, what is the most important lesson about the relationship between features, algorithms, and data in POS tagging?

a: Algorithm choice is the only factor that matters Explanation

Explanation

b: More data always solves any performance problem Explanation

Explanation

c: The optimal solution requires balancing algorithm sophistication, feature richness, and data quantity Explanation

Explanation

d: Simple features always outperform complex ones Explanation

Explanation