EntityRL

EntityRL is a software library designed to facilitate the application of reinforcement learning techniques to entity-related tasks in natural language processing. The framework provides modular components that allow researchers and developers to build agents capable of discovering optimal strategies for entity identification, linking, or disambiguation in text streams.

The core architecture of EntityRL consists of a reinforcement learning environment that receives textual input and returns observations in the form of token embeddings, part-of-speech tags, and contextual features. An agent, typically implemented as a neural network policy, selects actions such as labeling a token as a named entity, beginning a new entity span, or confirming a disambiguation choice. Rewards are engineered to reflect precision, recall, and linking accuracy, encouraging agents that balance noise reduction with comprehensive coverage.

EntityRL supports several standard reinforcement learning algorithms, including policy gradients, Q‑learning, and actor‑critic methods. The library is written in Python and leverages popular machine learning libraries such as PyTorch or TensorFlow for neural network construction. It also provides utilities for training pipelines, checkpoint management, and evaluation on benchmark datasets like CoNLL‑2003 and OntoNotes 5.0.

Researchers have utilized EntityRL to improve span detection in named entity recognition, as well as entity linking in knowledge base completion tasks. Through its flexible API, the library promotes experimentation with novel reward designs, hierarchical policies, and multi‑task learning approaches. EntityRL has been cited in a number of NLP conferences and is maintained as an open‑source project on GitHub.