Advances in Structured Prediction for Natural Language Processing
U – Carnegie Mellon University –
This thesis proposes new models and algorithms for structured output prediction, with an emphasis on natural language processing applications. We advance in two fronts: in the inference problem, whose aim is to make a prediction given a model, and in the learning problem, where the model is trained from data.
For inference, we make a paradigm shift, by considering rich models with global features and constraints, representable as constrained graphical models. We introduce a new approximate decoder that ignores global effects caused by the cycles of the graph. This methodology is then applied to syntactic analysis of text, yielding a new framework which we call “turbo parsing,” with state-of-the-art results.
For learning, we consider a family of loss functions encompassing conditional random fields, support vector machines and the structured perceptron, for which we provide new online algorithms that dispense with learning rate hyperparameters. We then focus on the regularizer, which we use for promoting structured sparsity and for learning structured predictors with multiple kernels. We introduce online proximal-gradient algorithms that can explore large feature spaces efficiently, with minimal memory consumption. The result is a new framework for feature template selection yielding compact and accurate models.
Date: 2012-May-04 Time: 15:00:00 Room: 336
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Research data repositories and tools for human genomics data sharing
Inform the human research community of the status and availability of BioData.pt Local EGA and discuss its need and usability challenges.
The European Genome-phenome Archive (EGA) is a repository for all sequence and genotype experiment types, including case-control, population, and family studies. The EGA will serve as a permanent archive that will archive several levels of data, including the raw data (which could, for example, be re-analysed in the future by other algorithms) as well as the genotype calls provided by the submitters.
Responding to national regulations over human data sharing and other constraints, BioData.pt deploys and operates a Local EGA instance and tools that allow data discovery of genomic and phenoclinic data, following the GA4GH standard and international best practices.
This workshop aims at informing the human research community of the status and availability of BioData.pt Local EGA and discuss from several perspectives its need and usability challenges.
Further details and registration are available here.
OLISSIPO Summer School in Lisbon | Computational phylogenetics to analyse the evolution of cells and communities
We are happy to announce the OLISSIPO Summer School on Computational phylogenetics to analyse the evolution of cells and communities, which will be held in Lisbon, Portugal, at INESC-ID, between July 2-7, 2023.
David Posada, University of Vigo (class)
João Alves, University of Vigo (hands-on)
Nadia El-Mabrouk, Université de Montréal (class)
Mattéo Delabre, Université de Montréal (hands-on)
Ran Libeskind-Hadas, Claremont McKenna College (class and hands-on)
Russell Schwartz, Carnegie Mellon University (class and hands-on)
See the preliminary agenda at: https://olissipo.inesc-id.pt/tree-tango-school
Registration is mandatory. You can register at: https://forms.gle/VsASFHW5E7MJvaCc9
The registration fee is 250€ for students and OLISSIPO members and 350€ for postdocs or other researchers (meals indicated at the schedule of the school are included, accommodation and flights are not). All details will be made available upon registration.
We will have slots for flash talks (3-10 min depending on the number of submissions) to present yourself and the work you have been developing in your research.
The 13th Lisbon Machine Learning School | LxMLS 2023
The Lisbon Machine Learning Summer School (LxMLS) takes place yearly at Instituto Superior Técnico (IST). LxMLS 2023 will be a 6-day event (14-20 July, 2023), scheduled to take place as an in-person event.
The school covers a range of machine learning topics, from theory to practice, that are important in solving natural language processing problems arising in different application areas. It is organized jointly by Instituto Superior Técnico (IST), a leading Engineering and Science school in Portugal, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), the Lisbon ELLIS Unit for Learning and Intelligent Systems (LUMLIS), Unbabel, Zendesk, and IBM Research.
Check online for information about past editions: LxMLS 2011, LxMLS 2012, LxMLS 2013, LxMLS 2014, LxMLS 2015, LxMLS 2016, LxMLS 2017, LxMLS 2018, LxMLS 2019, LxMLS 2020, LxMLS 2021, LxMLS 2022 (you can also watch the videos of the lectures for 2016, 2017, 2018, and 2020).
31st International Conference on Information Systems Development (ISD 2023)
The 31st International Conference on Information Systems Development (ISD 2023) conference provides a forum for research and developments in the field of information systems. The theme of ISD 2023 is “Information systems development, organizational aspects and societal trends”. New trends in developing information systems emphasize the continuous collaboration between developers and operators in order to optimize the software delivery time. The conference promotes research on methodological and technological issues and how IS developers and operators are transforming organizations and society through information systems.
The ISD 2023 conference held this year also provides an opportunity for researchers and practitioners to promote their research, practical experience, and to discuss issues related to Information Systems through papers, posters, and journal-first paper presentations.
ISD 2023 will be hosted by Instituto Superior Técnico, in Lisbon, Portugal, on August 30–September 1, 2023.