A Joint Model for Quotation Attribution and Coreference Resolution

2014 ◽  
Author(s):  
Mariana S. C. Almeida ◽  
Miguel B. Almeida ◽  
André F. T. Martins
Keyword(s):  
Joint Model ◽  
Coreference Resolution

scholarly journals A Joint Model for Entity Analysis: Coreference, Typing, and Linking

2014 ◽  
Vol 2 ◽  
pp. 477-490 ◽  
Author(s):  
Greg Durrett ◽  
Dan Klein
Keyword(s):  
State Of The Art ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Joint Modeling ◽  
Joint Model ◽  
Entity Recognition ◽  
Coreference Resolution ◽  
Entity Linking ◽  
Semantic Type ◽  
Named Entity

We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.

Going Hybrid: A Joint Model for Temperature and Natural Gas

2015 ◽  
Author(s):  
Roberto Baviera ◽  
Teodoro Federico Mainetti
Keyword(s):  
Natural Gas ◽  
Joint Model

Multimodal Coreference Resolution for Exploratory Data Visualization Dialogue: Context-Based Annotation and Gesture Identification

2017 ◽  
Author(s):  
Abhinav Kumar ◽  
Jillian Aurisano ◽  
Barbara Di Eugenio ◽  
Andrew Johnson ◽  
Abeer Alsaiari ◽  
...  
Keyword(s):  
Data Visualization ◽  
Coreference Resolution ◽  
Exploratory Data

scholarly journals Flexible multivariate joint model of longitudinal intensity and binary process for medical monitoring of frequently collected data

2021 ◽  
Author(s):  
Resmi Gupta ◽  
Jane C. Khoury ◽  
Mekibib Altaye ◽  
Roman Jandarov ◽  
Rhonda D. Szczesniak
Keyword(s):  
Joint Model ◽  
Medical Monitoring ◽  
Binary Process

Disease mapping of biomarkers for breast cancer in Tehran using spatial joint model: A Bayesian perspective

2021 ◽  
pp. 1-26
Author(s):  
T. Baghfalaki ◽  
M. Kamarehee ◽  
M. Ganjali ◽  
A. Shabbak ◽  
M. Khayamzadeh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Disease Mapping ◽  
Joint Model

scholarly journals Competing risks joint models using R-INLA

2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue
Keyword(s):  
Competing Risks ◽  
Count Data ◽  
Joint Model ◽  
Point Of View ◽  
Spatial Structures ◽  
Joint Models ◽  
Hazard Functions ◽  
Longitudinal Count Data ◽  
Non Gaussian ◽  
Made In

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

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