Understanding Coreference Resolution and Ambiguity

sabbir896

Understanding Coreference Resolution and Ambiguity (approx. 300 words)
1.1 Defining Coreference Resolution in NLP
1.2 The Significance of Ambiguity Handling in Coreference Resolution
1.3 The Role of Neural Models in Advancing Coreference Resolution

Early Neural Approaches to Coreference Resolution (approx. 400 words)
2.1 Neural Network-based Mention Pair Models
2.2 The Challenges of Ambiguity in Early Neural Approaches
2.3 Limitations of Early Neural Coreference Resolution Models
2.4 The Need for Contextual Understanding in Ambiguity Handling

Deep Learning Ghost Mannequin Service for Coreference Resolution (approx. 600 words)
3.1 Neural Networks and Word Embeddings in Coreference Resolution
3.2 Attention Mechanisms for Ambiguity Resolution
3.3 Resolving Ambiguity with Contextual Embeddings
3.4 Integrating Deep Learning with Rule-based Approaches

Transformer-Based Coreference Resolution Models (approx. 400 words)
4.1 The Rise of Transformers in NLP
4.2 Leveraging Transformer Architecture for Coreference Resolution
4.3 BERT (Bidirectional Encoder Representations from Transformers) in Ambiguity Handling
4.4 GPT (Generative Pre-trained Transformer) and Coreference Resolution.




Large-Scale Pre-training for Ambiguity Handling (approx. 300 words)
5.1 The Role of Pre-training in Neural Coreference Resolution
5.2 BERT-based Coreference Resolution Systems
5.3 Fine-Tuning and Transfer Learning for Ambiguity Handling
5.4 Impact of Large-Scale Pre-training on Coreference Resolution Performance.