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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 BASE Show 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 Abstract: This dissertation focuses on effective combination of data-driven natural language processing (NLP) approaches with linguistic knowledge sources that are based on manual text annotation or word grouping according to semantic commonalities. I gainfully apply fine-grained linguistic soft constraints -- of syntactic or semantic nature -- on statistical NLP models, evaluated in end-to-end state-of-the-art statistical machine translation (SMT) systems. The introduction of semantic soft constraints involves intrinsic evaluation on word-pair similarity ranking tasks, extension from words to phrases, application in a novel distributional paraphrase generation technique, and an introduction of a generalized framework of which these soft semantic and syntactic constraints can be viewed as instances, and in which they can be potentially combined. Fine granularity is key in the successful combination of these soft constraints, in many cases. I show how to softly constrain SMT models by adding fine-grained weighted features, each preferring translation of only a specific syntactic constituent. Previous attempts using coarse-grained features yielded negative results. I also show how to softly constrain corpus-based semantic models of words (“distributional profiles”) to effectively create word-sense-aware models, by using semantic word grouping information found in a manually compiled thesaurus. Previous attempts, using hard constraints and resulting in aggregated, coarse-grained models, yielded lower gains. A novel paraphrase generation technique incorporating these soft semantic constraints is then also evaluated in a SMT system. This paraphrasing technique is based on the Distributional Hypothesis. The main advantage of this novel technique over current “pivoting” techniques for paraphrasing is the independence from parallel texts, which are a limited resource. The evaluation is done by augmenting translation models with paraphrase-based translation rules, where fine-grained scoring of paraphrase-based rules yields significantly higher gains. The model augmentation includes a novel semantic reinforcement component: In many cases there are alternative paths of generating a paraphrase-based translation rule. Each of these paths reinforces a dedicated score for the “goodness” of the new translation rule. This augmented score is then used as a soft constraint, in a weighted log-linear feature, letting the translation model learn how much to “trust” the paraphrase-based translation rules. The work reported here is the first to use distributional semantic similarity measures to improve performance of an end-to-end phrase-based SMT system. The unified framework for statistical NLP models with soft linguistic constraints enables, in principle, the combination of both semantic and syntactic constraints -- and potentially other constraints, too -- in a single SMT model. Keyword: computational linguistics; Computer Science; hybrid; Language; Linguistics; paraphrase generation; semantic distance; soft constraints; statistical machine translation URL: http://hdl.handle.net/1903/9861 BASE Hide 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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