Impact of Coreference Resolution on Slot Filling
This work addresses slot filling tasks for NLP researchers, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of slot filling in natural language processing by investigating the impact of coreference resolution, showing that automatic coreference resolution systems improve performance in an end-to-end setting, and it published KBPchains, a resource of automatically extracted coreference chains from the TAC source corpus.
In this paper, we demonstrate the importance of coreference resolution for natural language processing on the example of the TAC Slot Filling shared task. We illustrate the strengths and weaknesses of automatic coreference resolution systems and provide experimental results to show that they improve performance in the slot filling end-to-end setting. Finally, we publish KBPchains, a resource containing automatically extracted coreference chains from the TAC source corpus in order to support other researchers working on this topic.