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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 – 5 of 5 1 The Limitations of Limited Context for Constituency Parsing ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Li, Yuchen; Risteski, Andrej. - : Underline Science Inc., 2021 BASE Show details 2 On Learning Language-Invariant Representations for Universal Machine Translation ... Zhao, Han; Hu, Junjie; Risteski, Andrej. - : arXiv, 2020 BASE Show details 3 Extending and Improving Wordnet via Unsupervised Word Embeddings ... Khodak, Mikhail; Risteski, Andrej; Fellbaum, Christiane. - : arXiv, 2017 BASE Show details 4 Linear Algebraic Structure of Word Senses, with Applications to Polysemy ... Arora, Sanjeev; Li, Yuanzhi; Liang, Yingyu. - : arXiv, 2016 BASE Show details 5 A Latent Variable Model Approach to PMI-based Word Embeddings ... Arora, Sanjeev; Li, Yuanzhi; Liang, Yingyu; Ma, Tengyu; Risteski, Andrej. - : arXiv, 2015 Abstract: Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model of~\citet{mnih2007three}. The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by~\citet{mikolov2013efficient} and many subsequent papers. Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space. ... : Appear in Transactions of the Association for Computational Linguistics (TACL), 2016 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML URL: https://dx.doi.org/10.48550/arxiv.1502.03520 https://arxiv.org/abs/1502.03520 BASE Hide details

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