# 2023-12-01 · final · recovered AI paper

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/legacy-f23

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers-chapter/legacy-f23/

Previous examination · complete worked answers

1. [Q1a · WSD and pronoun resolution](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1a/)
2. [Q1b · WordNet and its hierarchy](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1b/)
3. [Q1c · Sentiment feature extraction](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1c/)
4. [Q1d · Binary, multiclass and multilabel](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1d/)
5. [Q2a · Text corpus structure and applications](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q2a/)
6. [Q2b · Lesk versus Walker and bank disambiguation](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q2b/)
7. [Q3a · Hobbs algorithm and limitations](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q3a/)
8. [Q3b · Stemming and lemmatization](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q3b/)
9. [Q4a · Lexical and vector semantics](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q4a/)
10. [Q4b · Three-document TF–IDF](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q4b/)
11. [Q5a · Word embeddings and CBOW](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q5a/)
12. [Q5b · HMM tables and given path](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q5b/)
13. [Q6a · Text classification pipeline](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q6a/)
14. [Q6b · VSM, TF and IDF in retrieval](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q6b/)