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- W2983381688 abstract "Question Answering (QA) is always the key issue in the Natural Language Process (NLP). This paper mainly researches the question answering model based on Bi-directional Long Short Term Memory networks (Bi-LSTM), with the use of the WebQA dataset to train the model. Experiment results show that ACC@1 in Bi-LSTM model is 55.00%, ACC@3 is 73.24%, and ACC@10 reaches to 86.64%. The Bi-LSTM model outperforms Best Match 25 (BM25) algorithm in all of the three metrics, which proves the superiority of Bi-LSTM model." @default.
- W2983381688 created "2019-11-22" @default.
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- W2983381688 date "2019-10-01" @default.
- W2983381688 modified "2023-10-18" @default.
- W2983381688 title "Research on Question Answering Technology Based on Bi- LSTM" @default.
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- W2983381688 doi "https://doi.org/10.1088/1742-6596/1325/1/012009" @default.
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