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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback ... Zhu, Yunchang; Pang, Liang; Lan, Yanyan. - : arXiv, 2022 BASE Show details 2 A Linguistic Study on Relevance Modeling in Information Retrieval ... Fan, Yixing; Guo, Jiafeng; Ma, Xinyu. - : arXiv, 2021 BASE Show details 3 Semantic Regularities in Document Representations ... Sun, Fei; Guo, Jiafeng; Lan, Yanyan; Xu, Jun; Cheng, Xueqi. - : arXiv, 2016 Abstract: Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language. This allows vector-oriented reasoning based on simple linear algebra between words. Since many different methods have been proposed for learning document representations, it is natural to ask whether there is also linear structure in these learned representations to allow similar reasoning at document level. To answer this question, we design a new document analogy task for testing the semantic regularities in document representations, and conduct empirical evaluations over several state-of-the-art document representation models. The results reveal that neural embedding based document representations work better on this analogy task than conventional methods, and we provide some preliminary explanations over these observations. ... : 6 pages ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1603.07603 https://dx.doi.org/10.48550/arxiv.1603.07603 BASE Hide details

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