摘要：The slot filling task aims at extracting answers for queries about entities from text,
such as “Who founded Apple”. In this paper, we focus on the relation classification
component of a slot filling system. We propose type-aware convolutional neural networks
to benefit from the mutual dependencies between entity and relation classification. In
particular, we explore different ways of integrating the named entity types of the relation
arguments into a neural network for relation classification, including a joint training and
a structured prediction approach. To the best of our knowledge, this is the first study on
type-aware neural networks for slot filling. The type-aware models lead to the best results
of our slot filling pipeline. Joint training performs comparable to structured prediction. To
understand the impact of the different components of the slot filling pipeline, we perform
a recall analysis, a manual error analysis and several ablation studies. Such analyses
are of particular importance to other slot filling researchers since the official slot filling
evaluations only assess pipeline outputs. The analyses show that especially coreference
resolution and our convolutional neural networks have a large positive impact on the final
performance of the slot filling pipeline. The presented models, the source code of our
system as well as our coreference resource is publicy available.
其他摘要：The slot filling task aims at extracting answers for queries about entities from text, such as "Who founded Apple". In this paper, we focus on the relation classification component of a slot filling system. We propose type-aware convolutional neural networks to benefit from the mutual dependencies between entity and relation classification. In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach. To the best of our knowledge, this is the first study on type-aware neural networks for slot filling. The type-aware models lead to the best results of our slot filling pipeline. Joint training performs comparable to structured prediction. To understand the impact of the different components of the slot filling pipeline, we perform a recall analysis, a manual error analysis and several ablation studies. Such analyses are of particular importance to other slot filling researchers since the official slot filling evaluations only assess pipeline outputs. The analyses show that especially coreference resolution and our convolutional neural networks have a large positive impact on the final performance of the slot filling pipeline. The presented models, the source code of our system as well as our coreference resource is publicly available.
