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A Deep Top-K Relevance Matching Model for Ad-hoc Retrieval.

, , , , , , , and . CCIR, volume 11168 of Lecture Notes in Computer Science, page 16-27. Springer, (2018)

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A Comparison Between Term-Based and Embedding-Based Methods for Initial Retrieval., , , , , and . CCIR, volume 11168 of Lecture Notes in Computer Science, page 28-40. Springer, (2018)An Adaptive Framework for Conversational Question Answering., , , , , and . AAAI, page 10041-10042. AAAI Press, (2019)A Review on Question Generation from Natural Language Text., , , , and . ACM Trans. Inf. Syst., 40 (1): 14:1-14:43 (2022)Dual-factor Generation Model for Conversation., , , , and . ACM Trans. Inf. Syst., 38 (3): 31:1-31:31 (2020)Outline Generation: Understanding the Inherent Content Structure of Documents., , , , and . SIGIR, page 745-754. ACM, (2019)Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations., , , , , and . CoRR, (2018)Semantic Matching by Non-Linear Word Transportation for Information Retrieval., , , and . CIKM, page 701-710. ACM, (2016)A Deep Relevance Matching Model for Ad-hoc Retrieval., , , and . CoRR, (2017)Modeling Diverse Relevance Patterns in Ad-hoc Retrieval., , , , , and . SIGIR, page 375-384. ACM, (2018)Learning Visual Features from Snapshots for Web Search., , , , , and . CIKM, page 247-256. ACM, (2017)