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_3: Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features. Such combination results in large numbers of features, which can lead to over-fitting. We present a new model that represents complex lexical features---comprised of parts for words, contextual information and labels---in a tensor that captures conjunction information among these parts. We apply low-rank tensor approximations to the corresponding parameter tensors to reduce the parameter space and improve prediction speed. Furthermore, we investigate two methods for handling features that include @math -grams of mixed lengths. Our model achieves state-of-the-art results on tasks in relation extraction, PP-attachment, and preposition disambiguation.
One strong assumption mentioned above in the most RE methods is that a fixed (i.e., closed) set of relation types is given as an prior knowledge, thus no zero-shot learning capability (i.e. detecting new relations that did not occur during training) is required. Another commonality among these RE methods is that the relation set is usually not large. Here are some examples. The widely used ACE2005 has 11 32 coarse fine-grained relations; SemEval2010 Task8 has 19 relations; TAC-KBP2015 has 74 relations although it considers open-domain Wikipedia relations. Compared to that, KBQA usually has thousands of relations. Thus most RE approaches may not work well by directly being adapted to large number of relations or unseen relations. The relation embeddings in a low-rank tensor method were used @cite_3 . However it is still using supervised way to train their relation embeddings and relation set used in the experiments is still not large.