Relation Extraction with Type-aware Map Memories of Word Dependencies

Relation extraction is an important task in information extraction and retrieval that aims to extract relations among the given entities from running texts. To achieve a good performance for this task, previous studies have shown that a good modeling of the contextual information is required, where the dependency tree of the input sentence can be a beneficial source among different types of contextual information. However, most of these studies focus on the dependency connections between words with limited attention paid to exploiting dependency types. In addition, they often treat different dependency connections equally in modeling so that suffer from the noise (inaccurate dependency parses) in the auto-generated dependency tree. In this paper, we propose a neural approach for relation extraction, with type-aware map memories (TaMM) for encoding dependency types obtained from an off-theshelf dependency parser for the input sentence. Specifically, for each word in an entity, TaMM maps all associated words along with the dependencies among them to memory slots and then assigns a weight to each slot according to its contribution to relation extraction. Our approach not only leverages dependency connections and types between words, but also distinguishes reliable dependency information from noisy ones and appropriately model them. The effectiveness of our approach is demonstrated by the experiments on two English benchmark datasets, where our approach achieves state-ofthe-art performance on both datasets.