Question Answering Over Freebase With Multi Column Convolutional Neural Networks

Question Answering Over Freebase With Multi Column Convolutional Neural Networks. By applying a convolutional neural network model to match questions and predicate sequences and a type constraint to filter candidate answers, our method achieves an average. In this task, a key step is how.

We use freebase as the knowledge base and conduct extensive. Using question representations obtained by different column networks to query the nearest neighbors. The blue social bookmark and publication sharing system.

まず質問文の分散表現をに関して、Answer Path, Answer Type, Answer Context の3つの観点(Column)を導入する。それぞれの意味は以下の通り。それぞれは同一次元のベ.

From left to right, the three columns are used to analyze information about. In this task, a key step is how. This task, simple question answering (simpleqa), can be addressed via a.

Using Question Representations Obtained By Different Column Networks To Query The Nearest Neighbors.

Each question can acquire the answer from a single fact of form (subject, predicate, object) in freebase. We use freebase as the knowledge base and conduct extensive experiments on the. The blue social bookmark and publication sharing system.

We Use Freebase As The Knowledge Base And Conduct Extensive.

By applying a convolutional neural network model to match questions and predicate sequences and a type constraint to filter candidate answers, our method achieves an average. Question answering (qa) over knowledge base (kb) aims to provide a structured answer from a knowledge base to a natural language question.

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QUESTIONSC