ACL2016

Representation Learning

1. Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
2. Pointing the Unknown Words
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3. Literal and Metaphorical Senses in Compositional Distributional Semantic Models
4. Language Transfer Learning for Supervised Lexical Substitution
5. Idiom Token Classification using Sentential Distributed Semantics
6. Adaptive Joint Learning of Compositional and Non-Compositional Phrase Embeddings
7. Compressing Neural Language Models by Sparse Word Representations
8. On the Role of Seed Lexicons in Learning Bilingual Word Embeddings
9. Query Expansion with Locally-Trained Word Embeddings
10. Improved Representation Learning for Question Answer Matching
11. CSE: Conceptual Sentence Embeddings based on Attention Model
12. How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions
13. Generative Topic Embedding: a Continuous Representation of Documents
14. Embeddings for Word Sense Disambiguation: An Evaluation Study
15. Siamese CBOW: Optimizing Word Embeddings for Sentence Representations
16. Learning Semantically and Additively Compositional Distributional Representations
17. Larger-Context Language Modelling with Recurrent Neural Network
18. Learning Word Meta-Embeddings
19. Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text
20. Investigating Language Universal and Specific Properties in Word Embeddings
21. Stack-propagation: Improved Representation Learning for Syntax
22. Morphological Smoothing and Extrapolation of Word Embeddings
23. Cross-lingual Models of Word Embeddings: An Empirical Comparison
24. Strategies for Training Large Vocabulary Neural Language Models
25. Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time
26. Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization
27. Improving cross-domain n-gram language modelling with skipgrams
28. Tweet2Vec: Character-Based Distributed Representations for Social Media
29. Word Embeddings with Limited Memory
30. Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations
31. Word Embedding Calculus in Meaningful Ultradense Subspaces
32. Is “Universal Syntax” Universally Useful for Learning Distributed Word Representations?

Sentiment Analysis

1. Metaphor Detection with Topic Transition, Emotion and Cognition in Context
2. Sentiment Domain Adaptation with Multiple Sources
3. AraSenTi: Large-Scale Twitter-Specific Arabic Sentiment Lexicons
4. Modeling Social Norms Evolution for Personalized Sentiment Classification
5. Harnessing Cognitive Features for Sarcasm Detection
6. Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning
7. Cross-domain Text Classification with Multiple Domains and Disparate Label Sets
8. Evaluating Sentiment Analysis in the Context of Securities Trading
9. Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text
10. Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model
11. Finding Optimists and Pessimists on Twitter

Others

1. Investigating LSTMs for Joint Extraction of Opinion Entities and Relations
2. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
3. Deep multi-task learning with low level tasks supervised at lower layers
4. User Embedding for Scholarly Microblog Recommendation
5. The Social Impact of Natural Language Processing