Centrala begrepp
Improving relation extraction performance with text representation learning.
Sammanfattning
Recent years have seen a rapid development in Information Extraction, especially in Relation Extraction. This thesis focuses on improving supervised approaches with unsupervised pre-training to address the challenge of limited training data. By utilizing distributed text representation features, the performance of logistic classification models for relation extraction can be enhanced, particularly for relations with minimal training instances.
The content covers concepts like knowledge base, ontology, and different approaches to relation extraction such as supervised, unsupervised, semi-supervised, and distant supervision. It also delves into hand-crafted features like part of speech tags, named entity tags, context words, and dependency paths. The chapter discusses neural networks and their application in text representation learning for relation extraction tasks.
Key points include the importance of feature selection in baseline systems, novel representation learning models like Shortest Dependency Path LSTM, and experiments to evaluate model performance based on different datasets and hyperparameters.
Statistik
Recent years have seen a rapid development in Information Extraction.
Supervised learning approaches have good performance but face challenges with limited data.
Unsupervised pre-training models aim to improve supervised approaches.
Feature selection is crucial in improving baseline systems.
Neural networks play a key role in text representation learning for relation extraction.
Citat
"The intuition behind Distant Supervision Approach is that any sentence containing entities participating in a known Freebase relation likely expresses that relation." - Mintz et al., 2009