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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 – 10 of 10 1 Data-to-text Generation by Splicing Together Nearest Neighbors ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Backurs, Arturs; Stratos, Karl. - : Underline Science Inc., 2021 BASE Show details 2 Mining Knowledge for Natural Language Inference from Wikipedia Categories ... Chen, Mingda; Chu, Zewei; Stratos, Karl. - : arXiv, 2020 BASE Show details 3 Reconstruction of Word Embeddings from Sub-Word Parameters ... Stratos, Karl. - : arXiv, 2017 BASE Show details 4 A Sub-Character Architecture for Korean Language Processing ... Stratos, Karl. - : arXiv, 2017 BASE Show details 5 Spectral Methods for Natural Language Processing Stratos, Karl. - 2016 Abstract: Many state-of-the-art results in natural language processing (NLP) are achieved with statistical models involving latent variables. Unfortunately, computational problems associated with such models (for instance, finding the optimal parameter values) are typically intractable, forcing practitioners to rely on heuristic methods without strong guarantees. While heuristics are often sufficient for empirical purposes, their de-emphasis on theoretical aspects has certain negative ramifications. First, it can impede the development of rigorous theoretical understanding which can generate new ideas and algorithms. Second, it can lead to black art solutions that are unreliable and difficult to reproduce. In this thesis, we argue that spectral methods---that is, methods that use singular value decomposition or other similar matrix or tensor factorization---can effectively remedy these negative ramifications. To this end, we develop spectral methods for two unsupervised language processing tasks. The first task is learning lexical representations from unannotated text (e.g., hierarchical clustering of a vocabulary). The second task is estimating parameters of latent-variable models used in NLP applications (e.g., for unsupervised part-of-speech tagging). We show that our spectral algorithms have the following advantages over previous methods: 1. The algorithms provide a new theoretical framework that is amenable to rigorous analysis. In particular, they are shown to be statistically consistent. 2. The algorithms are simple to implement, efficient, and scalable to large amounts of data. They also yield results that are competitive with the state-of-the-art. Keyword: Computer algorithms; Computer science; Natural language processing (Computer science) URL: https://doi.org/10.7916/D87P8ZJW BASE Hide details 6 Experiments With Spectral Learning of Latent-Variable PCFGs Cohen, Shay B; Stratos, Karl; Collins, Michael... In: Statistics Papers (2013) BASE Show details 7 Episodic Logic: Natural Logic + Reasoning Stratos, Karl; Schubert, Lenhart K.; Gordon, Jonathan (1985 - ). - : Springer, 2011 BASE Show details 8 Episodic Logic: Natural Logic + Reasoning Stratos, Karl; Schubert, Lenhart K.; Gordon, Jonathan (1985 - ). - : Springer, 2011 BASE Show details 9 Towards Adequate Knowledge and Natural Inference Stratos, Karl; Schubert, Lenhart K.; Rubinoff, Adina. - 2011 BASE Show details 10 Towards Adequate Knowledge and Natural Inference Rubinoff, Adina; Gordon, Jonathan (1985 - ); Schubert, Lenhart K.. - : Association for the Advancement of Artificial Intelligence, 2011 BASE Show details

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