Avirup Sil

Re-ranking for joint named-entity recognition and linking

Recognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a Named-Entity Recognition (NER) system to find the boundaries of mentions in text, and an Entity Linking (EL) system to connect the mentions to entries in structured or semi-structured repositories like Wikipedia. However, the two tasks are tightly coupled, and each type of system can benefit significantly from the kind of information provided by the other. We present a joint model for NER and EL, called NEREL, that takes a large set of candidate mentions from typical NER systems and a large set of candidate entity links from EL systems, and ranks the candidate mention-entity pairs together to make joint predictions. In NER and EL experiments across three datasets, NEREL significantly outperforms or comes close to the performance of two state-of-the-art NER systems, and it outperforms 6 competing EL systems. On the benchmark MSNBC dataset, NEREL provides a 60% reduction in error over the next-best NER system and a 68% reduction in error over the next-best EL system.

Exploring re-ranking approaches for joint named-entityrecognition and linking

Recognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a Named-Entity Recognition (NER) system to find the boundaries of mentions in text, and an Entity Linking (EL) system to connect the mentions to entries in structured or semi-structured repositories like Wikipedia. However, the two tasks are tightly coupled, and each type of system can benefit significantly from the kind of information provided by the other. In this proposal, we present a joint model for NER and EL, called NEREL, that takes a large set of candidate mentions from typical NER systems and a large set of candidate entity links from EL systems, and ranks the candidate mention-entity pairs together to make joint predictions. In our initial NER and EL experiments across three datasets, NEREL significantly outperforms or comes close to the performance of two state-of-the-art NER systems, and it outperforms 6 competing EL systems. On the benchmark MSNBC dataset, NEREL provides a 60% reduction in error over the next-best NER system and a 68% reduction in error over the next-best EL system.