Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.
@cite_24: Relation classification is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text. We propose a novel convolutional neural network architecture for this task, relying on two levels of attention in order to better discern patterns in heterogeneous contexts. This architecture enables endto-end learning from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments show that our model outperforms previous state-of-the-art methods, including those relying on much richer forms of prior knowledge.
@cite_18: Relation extraction suffers from a performance loss when a model is applied to out-of-domain data. This has fostered the development of domain adaptation techniques for relation extraction. This paper evaluates word embeddings and clustering on adapting feature-based relation extraction systems. We systematically explore various ways to apply word embeddings and show the best adaptation improvement by combining word cluster and word embedding information. Finally, we demonstrate the effectiveness of regularization for the adaptability of relation extractors.
@cite_22: Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) handcrafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a loglinear classifier with hand-crafted features gives state-of-the-art results. We made our implementation available for general use 1 .
Another significant difference between relation detection in KBQA and general RE is that general RE research works on the condition that the two argument entities are both available. Thus it usually can learn from features @cite_22 @cite_18 or attention mechanisms @cite_24 based on the entity information (e.g. entity types or entity embeddings). In contrast relation detection for KBQA mostly does not have this information: (1) one question usually contains single argument (the topic entity) and (2) one KB entity could have multiple types (type vocabulary size larger than 1,500). This makes KB entity typing itself a difficult problem so no previous used entity information in the relation detection model. Such entity information has been used in some KBQA systems as features for the final answer re-rankers.