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The authors analyze three critical components in training word embeddings: model, corpus, and training parameters. They systematize existing neural-network-based word embedding methods and experimentally compare them using the same corpus. They then evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks, and using it to initialize...
Question answering over knowledge bases is a challenging task for next-generation search engines. The core of this task is to understand the meaning of questions and translate them into structured language-based queries. Previous research has focused on a specific knowledge base with a constrained domain, but with the increase in the size and domain of existing knowledge bases, fulfilling this aim...
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