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Text representations using neural word embeddings have proven effective inmany NLP applications. Recent researches adapt the traditional word embeddingmodels to learn vectors of multiword expressions (concepts/entities). However,these methods are limited to textual knowledge bases (e.g., Wikipedia). In thispaper, we propose a novel and simple technique for integrating the knowledgeabout concepts from two large scale knowledge bases of different structure(Wikipedia and Probase) in order to learn concept representations. We adapt theefficient skip-gram model to seamlessly learn from the knowledge in Wikipediatext and Probase concept graph. We evaluate our concept embedding models on twotasks: (1) analogical reasoning, where we achieve a state-of-the-artperformance of 91% on semantic analogies, (2) concept categorization, where weachieve a state-of-the-art performance on two benchmark datasets achievingcategorization accuracy of 100% on one and 98% on the other. Additionally, wepresent a case study to evaluate our model on unsupervised argument typeidentification for neural semantic parsing. We demonstrate the competitiveaccuracy of our unsupervised method and its ability to better generalize to outof vocabulary entity mentions compared to the tedious and error prone methodswhich depend on gazetteers and regular expressions.

Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases