1. Seminar Topics and Projects
Università di Pisa
Seminar Topics and Projects
Giuseppe Attardi
Dipartimento di Informatica
Università di Pisa

2. Deep Learning Tokenizer
Depling 2016 challenge requires tokenizer for any of the Universal Dependency TreeBank
Build a DL tokenizer using Keras based on the approach of:
Basile, Valerio and Bos, Johan and Evang, Kilian A General-Purpose Machine Learning Method for Tokenization and Sentence Boundary Detection (2013),

3. Deep Learning POS for UD
Depling 2016 challenge requires tokenizer for any of the Universal Dependency TreeBank
Build a DL POS using CNN, for example a LSTM that uses word embeddings and possible charcater embeddings.

4. Deep Learning Morph Analyzer
Depling 2016 challenge requires tokenizer for any of the Universal Dependency TreeBank
Build a DL morphological analyzer that copmutes the morphology of each word, using Keras and charcaher embeddings.

5. UD extensions
Write scripts to extract additional relations from the analysis of UD parse trees

6. Convolutional Networks for Sentiment Analysis
Annotated Data: SemEval training set
Unannotated Data: 50 million tweets
Code: DeepNL,
Article: A. Severyn, A. Moschitti.UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification

7. POS tagging using Word Embeddings
Data: Evalita 2016
Embeddings:
Article: Stratos, M. Collins. Simple Semi-Supervised POS Tagging.

8. Negation/Speculation Extraction
Determine the scope of negative or speculative statements:
The lyso-platelet had no effect
MnlI-AluI could suppress the basal-level activity
Approach:
Classifier for identifying cues
Classifier to determine scope
Data
BioScope collection

9. Corpus of Product Reviews
Download reviews from online shops
Classify as positive/negative according to stars
Train classifier to assign score

10. Relation Extraction
Exploit word embeddings as features + extra hand-coded features
Use the Factor Based Compositional Embedding Model (FCM)
SemEval 2014 Relation Extraction data

11. Entity Linking with Embeddings
Experiment with technique:
R. Blanco, G. Ottaviano, E. Meiji Fast and Space-Efficient Entity Linking in Queries.
labs.yahoo.com/_c/uploads/WSDM-2015-blanco.pdf

12. Extraction of Semantic Hierarchies
Use word embeddings as measure of semantic distance
Use Wikipedia as source of text
Organism
Plant
Ranuncolacee
Aconitum

13. Suggested Topics for Seminars

14. Neural Reasoning
B. Peng, Z. Lu, H. Li, K.F. WongToward Neural Network-based Reasoning
A. Kumar et al.Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

15. Question Answering Bowl Competition (QANTA vs Jennings)
Iyyer et al. 2014: A Neural Network for Factoid Question Answering over Paragraphs
IBM Watson:
TAC:

16. Image Understanding
H. Y. Gao et al. Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering, NIPS, 2015.

17. Deep Learning Applications
Character RNNs on text and code
Morphology
Better Word Representations with Recursive Neural Networks for Morphology – Luong et al.
Polysemous words
Improving Word Representa8ons Via Global Context And Multiple Word Prototypes by Huang et al. 2012
Natural language Inference (Logic)
Question Answering
Image – Sentence mapping

18. Entity Linking Entity Kierarchy Embeddings

19. Deep Learning tsunami over NLP
C. Manning

20. Opinion Mining
B. Liu. Sentiment Analisis and Subjectivity Handbook of NLP.

21. Semantic Role Labeling

22. DL for NLP Neural Machine Translation Natural Language from scratch
D. Bahdanau, K. Cho, Y. Bengio. Neural machine translation by jointly learning to align and translate.
Natural Language from scratch
Zhang, X., & LeCun, Y. (2015). Text Understanding from Scratch.