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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 Page: 1 2 3 4 Hits 1 – 20 of 79 1 Parallel processing in speech perception with local and global representations of linguistic context Brodbeck, Christian; Bhattasali, Shohini; Cruz Heredia, Aura AL... In: eLife (2022) BASE Show details 2 Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension ... 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; Bhattasali, Shohini; Resnik, Philip. - : Underline Science Inc., 2021 BASE Show details 3 Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task MacAvaney, Sean; Mittu, Anjali; Coppersmith, Glen. - 2021 BASE Show details 4 Connecting Documents, Words, and Languages Using Topic Models Yang, Weiwei. - 2019 BASE Show details 5 Assessing Composition in Sentence Vector Representations ... Ettinger, Allyson; Elgohary, Ahmed; Phillips, Colin. - : arXiv, 2018 BASE Show details 6 Relating lexical and syntactic processes in language: Bridging research in humans and machines Ettinger, Allyson. - 2018 BASE Show details 7 Guided Probabilistic Topic Models for Agenda-setting and Framing Nguyen, Viet An. - 2015 BASE Show details 8 Soft syntactic constraints for Arabic-English hierarchical phrase-based translation Resnik, Philip; Chiang, David; Marton, Yuval In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 26 (2012) 1-2, 137-157 BLLDB OLC Linguistik Show details 9 Crowdsourced Monolingual Translation Hu, Chang. - 2012 BASE Show details 10 Decision Tree-based Syntactic Language Modeling Filimonov, Denis. - 2011 BASE Show details 11 Modeling Dependencies in Natural Languages with Latent Variables Huang, Zhongqiang. - 2011 Abstract: In this thesis, we investigate the use of latent variables to model complex dependencies in natural languages. Traditional models, which have a fixed parameterization, often make strong independence assumptions that lead to poor performance. This problem is often addressed by incorporating additional dependencies into the model (e.g., using higher order N-grams for language modeling). These added dependencies can increase data sparsity and/or require expert knowledge, together with trial and error, in order to identify and incorporate the most important dependencies (as in lexicalized parsing models). Traditional models, when developed for a particular genre, domain, or language, are also often difficult to adapt to another. In contrast, previous work has shown that latent variable models, which automatically learn dependencies in a data-driven way, are able to flexibly adjust the number of parameters based on the type and the amount of training data available. We have created several different types of latent variable models for a diverse set of natural language processing applications, including novel models for part-of-speech tagging, language modeling, and machine translation, and an improved model for parsing. These models perform significantly better than traditional models. We have also created and evaluated three different methods for improving the performance of latent variable models. While these methods can be applied to any of our applications, we focus our experiments on parsing. The first method involves self-training, i.e., we train models using a combination of gold standard training data and a large amount of automatically labeled training data. We conclude from a series of experiments that the latent variable models benefit much more from self-training than conventional models, apparently due to their flexibility to adjust their model parameterization to learn more accurate models from the additional automatically labeled training data. The second method takes advantage of the variability among latent variable models to combine multiple models for enhanced performance. We investigate several different training protocols to combine self-training with model combination. We conclude that these two techniques are complementary to each other and can be effectively combined to train very high quality parsing models. The third method replaces the generative multinomial lexical model of latent variable grammars with a feature-rich log-linear lexical model to provide a principled solution to address data sparsity, handle out-of-vocabulary words, and exploit overlapping features during model induction. We conclude from experiments that the resulting grammars are able to effectively parse three different languages. This work contributes to natural language processing by creating flexible and effective latent variable models for several different languages. Our investigation of self-training, model combination, and log-linear models also provides insights into the effective application of these machine learning techniques to other disciplines. Keyword: Computer science; language modeling; latent variable models; machine translation; natural language processing; parsing; tagging URL: http://hdl.handle.net/1903/12295 BASE Hide details 12 Exploiting syntactic relationships in a phrase-based decoder: an exploration Hunter, Tim; Resnik, Philip In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 24 (2010) 2, 123-140 BLLDB OLC Linguistik Show details 13 Gibbs Sampling for the Uninitiated Resnik, Philip; Hardisty, Eric In: DTIC (2010) BASE Show details 14 Structured local exponential models for machine translation Subotin, Michael. - 2010 BASE Show details 15 A Formal Model of Ambiguity and its Applications in Machine Translation Dyer, Christopher. - 2010 BASE Show details 16 Extending Phrase-Based Decoding with a Dependency-Based Reordering Model Hunter, Tim; Resnik, Philip In: DTIC (2009) BASE Show details 17 Extending Phrase-Based Decoding with a Dependency-Based Reordering Model Resnik, Philip; Hunter, Tim. - 2009 BASE Show details 18 COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT Hawes, Timothy. - 2009 BASE Show details 19 Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models Marton, Yuval Yehezkel. - 2009 BASE Show details 20 Generalizing Word Lattice Translation Dyer, Christopher; Muresan, Smaranda; Resnik, Philip In: DTIC (2008) BASE Show details Page: 1 2 3 4

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