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window.loswp.translateUrl = "https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F14976667%2FA_Latent_Variable_Model_for_Generative_Dependency_Parsing%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":43681477,"identifier":"Attachment_43681477","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":14976667,"created_at":"2015-08-17T02:32:10.469-07:00","from_world_paper_id":141135311,"updated_at":"2024-11-14T09:17:30.406-08:00","_data":{"grobid_abstract":"We propose a generative dependency parsing model which uses binary latent variables to induce conditioning features. To define this model we use a recently proposed class of Bayesian Networks for structured prediction, Incremental Sigmoid Belief Networks. We demonstrate that the proposed model achieves state-of-the-art results on three different languages. We also demonstrate that the features induced by the ISBN's latent variables are crucial to this success, and show that the proposed model is particularly good on long dependencies.","publication_date":"2010,,","publication_name":"Text, Speech and Language Technology","grobid_abstract_attachment_id":"43681477"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Latent Variable Model for Generative Dependency Parsing","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [33971246]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div 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PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A Latent Variable Model for Generative Dependency Parsing</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="33971246" href="https://independent.academia.edu/JamesHenderson28"><img alt="Profile image of James Henderson" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />James Henderson</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2010, Text, Speech and Language Technology</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" 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We additionally replace LSTMs with GRUs and Elman units in our model and explore the effectiveness of our initialization method on individual gates constituting all three types of RNN units.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Simple LSTM model for Transition-based Dependency Parsing","attachmentId":96414269,"attachmentType":"pdf","work_url":"https://www.academia.edu/93767240/A_Simple_LSTM_model_for_Transition_based_Dependency_Parsing","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/93767240/A_Simple_LSTM_model_for_Transition_based_Dependency_Parsing"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="2600220" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2600220/Exact_inference_for_generative_probabilistic_non_projective_dependency_parsing">Exact inference for generative probabilistic non-projective dependency parsing</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="925063" href="https://coruna.academia.edu/CarlosG%C3%B3mezRodr%C3%ADguez">Carlos Gómez-Rodríguez</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Abstract We describe a generative model for non-projective dependency parsing based on a simplified version of a transition system that has recently appeared in the literature. 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In this paper, we present a novel extension of this class of spectral methods to learn dependency tree structures.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Spectral Dependency Parsing with Latent Variables","attachmentId":30775418,"attachmentType":"pdf","work_url":"https://www.academia.edu/2826419/Spectral_Dependency_Parsing_with_Latent_Variables","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2826419/Spectral_Dependency_Parsing_with_Latent_Variables"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":43681477,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":43681477,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_43681477" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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