Natural Language Processing summer course at Sabanci University

This summer Kemal Oflazer, Dilek Hakkani-Tur ve Gokhan Tur are offering a Statistical Natural Language Processing course at Sabanci University. A draft syllabus is included below.

STATISTICAL NLP CLASS:

Kemal Oflazer:

• Overview of NLP (2 hours)
• NLP Applications
• Processing pipeline: Basic steps and how they feed into each other and how they are used by applications

• Morphological Analysis (could be skipped or shortened) (2 hours)

• Introduction to Statistical Models, n-gram language modeling, (2hours)
• Applications to simple sequence problems (tagging English and/or deascifier)

• Morphological Disambiguation (applications to Turkish)

• HMMs (formal treatment (backward-forward + viterbi) + applications to tagging) (2-3 hours)

• CFGs and Probabilistic CFGs (3-4 hours)
• Inside-outside algorithm for training PCFGs
• Parsing with PCFGs
• Machine Translation (MT) (3-4 Hours)
• Brief overview Classical Symbolic MT
• Statistical Machine Translation
• Word-based Models
• Phrase-based Models
• Syntax-based models
• Dealing with Morphology in SMT

Dilek Hakkani-Tur:

Elements of Information Theory / Advanced Language Modeling and Applications

• Entropy/Perplexity/Mutual Information
• Noisy Channel Model
• Sequence classification / HMM
• Sample classification / Naive Bayes
• Smoothing
• Adaptation
Named Entity Extraction (NE)
• Using HMM for NE
• Using CRF for NE
• Using Boosting/MaxEnt/SVM for NE
• Spoken Language Understanding (SLU) as Template Filling
• HMM approaches (AT&T vs BBN)
• Hidden Vector State Models
• Latent Semantic Analysis
• Sample-classification based (Boosting/MaxEnt/Decision Trees)
• Summarization
• Greedy Algorithms, MMR
• TextRank/LexRank
• Classification based extractive summarization
• Global Models for Summarization: Linear Programming approaches
• Question Answering
• Spoken Dialog Systems and Dialog Management (DM)
• Dialog Systems
• DM
• Finite State Models
• Agent Models
• Reinforcement Learning

Gokhan Tur

• Topic Classification
• Discriminative classification: SVM/Boosting
• Generative classification: language model, document similarity, vector-space-model
• Feature selection/transformation (LDA)
• Latent semantic indexing
• SLU as Intent Determination
• Semantic Role Labeling
• Robustness to ASR
• Topic Clustering
• K-Means
• Top/Down vs. Bottom/Up
• Topic Segmentation
• HMM
• TextTiling
• Markov Chains
• Sentence Segmentation
• HMM
• CRF
• Hybrid
• Active Learning/Semi-Supervised Learning/Unsupervised Learning/Model Adaptation/Robustness