31 мар. 2017 г. · This paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source.

This paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span ...

This paper proposes to tackle open- domain question answering using. Wikipedia as the unique knowledge source: the answer to any factoid question.

DrQA is a system for reading comprehension applied to open-domain question answering. In particular, DrQA is targeted at the task of machine reading at scale ( ...

This paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text ...

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a...

The input vectors consist of: • Word embeddings. • Exact match features: whether the word appears in question. • Token features: POS, NER, term frequency.

This approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect ...

The primary goal is to accurately and efficiently retrieve information in response to queries across a wide array of topics, without restriction to a predefined ...

14 дек. 2023 г. · Bibliographic details on Reading Wikipedia to Answer Open-Domain Questions.