Deep Learning for Semantic Parsing
نویسندگان
چکیده
Recently, we developed USP, the first approach for unsupervised semantic parsing [11]. We applied it to extracting a knowledge base from biomedical abstracts for question answering and found that it substantially outperforms state-of-the-art systems such as TextRunner and DIRT. In this paper, we show that USP can be viewed as learning a deep network for semantic parsing. The hidden units in the network represent clusters of meaning expressions, whereas the visible units represent dependency trees of input sentences. USP starts with a network where each atomic expression has its own cluster, and learns the final architecture by incrementally combining hidden units to abstract away syntactic and lexical variations of the same meaning. USP can be naturally generalized to a new approach for deep learning based on structure search; we discuss the implications of this.
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