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Differences in Chinese and Western tourists faced with Japanese hospitality: A natural language processing approach ...
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Zero-Shot Open Information Extraction using Question Generation and Reading Comprehension ...
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Knowledge Graph Completion with Text-aided Regularization ...
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Automatic extraction of requirements expressed in industrial standards : a way towards machine readable standards ? ...
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Can we aggregate human intelligence? an approach for human centric aggregation using ordered weighted averaging operators ...
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Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies ...
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Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning ...
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Personalized Transformer for Explainable Recommendation ...
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Learning Relation Alignment for Calibrated Cross-modal Retrieval ...
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Document Domain Randomization for Deep Learning Document Layout Extraction ...
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Linguistically Informed Masking for Representation Learning in the Patent Domain ...
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Improving Authorship Verification using Linguistic Divergence ...
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BIOPAK Flasher: Epidemic disease monitoring and detection in Pakistan using text mining ...
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Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings ...
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117 |
On the Calibration and Uncertainty of Neural Learning to Rank Models ...
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MULTIMODAL ANALYSIS: Informed content estimation and audio source separation ...
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119 |
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval ...
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A Linguistic Study on Relevance Modeling in Information Retrieval ...
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Abstract:
Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has always been critical challenges in both information science and computer science research areas. Along with the debate and exploration on relevance, IR has already become a core task in many real-world applications, such as Web search engines, question answering systems, conversational bots, and so on. While relevance acts as a unified concept in all these retrieval tasks, the inherent definitions are quite different due to the heterogeneity of these tasks. This raises a question to us: Do these different forms of relevance really lead to different modeling focuses? To answer this question, in this work, we conduct an empirical study on relevance modeling in three representative IR tasks, i.e., document retrieval, answer retrieval, and response retrieval. Specifically, we attempt to study the following two questions: 1) Does ...
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Keyword:
FOS Computer and information sciences; Information Retrieval cs.IR
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URL: https://arxiv.org/abs/2103.00956 https://dx.doi.org/10.48550/arxiv.2103.00956
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