Type-aware Convolutional Neural Networks for Slot Filling

TL;DR

This paper introduces type-aware convolutional neural networks for relation classification in slot filling, leveraging entity types to improve extraction accuracy, and demonstrates their effectiveness through comprehensive analysis.

Contribution

It is the first to propose and evaluate type-aware neural networks for slot filling, integrating entity types into relation classification.

Findings

Type-aware models outperform previous methods.

Joint training is as effective as structured prediction.

Coreference resolution significantly improves performance.

Abstract

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.