Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

The first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity... (read more)

PDF Abstract SEMEVAL 2018 PDF SEMEVAL 2018 Abstract

Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Entity Linking WebQSP-WD VCG F1 0.73 # 1

Methods used in the Paper


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