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Modeling Structured Data in Attention-based Models

Publications associées (65)

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Unsupervised Learning of Representations for Lexical Entailment Detection

Andreas Hug

Detecting lexical entailment plays a fundamental role in a variety of natural language processing tasks and is key to language understanding. Unsupervised methods still play an important role due to the lack of coverage of lexical databases in some domains ...

2018

Is lack of attention necessary for task-irrelevant perceptual learning?

Michael Herzog, Lukasz Grzeczkowski, Jessica Galliussi

Perceptual learning can occur for a feature irrelevant to the training task, when it is sub-threshold and outside of the focus of attention (task-irrelevant perceptual learning, TIPL); however, TIPL does not occur when the task-irrelevant feature is supra- ...

PERGAMON-ELSEVIER SCIENCE LTD2018

Word Sequence Modeling using Deep Learning

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For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

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Building Word Embeddings for Solving Natural Language Processing

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Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

École Polytechnique Fédérale de Lausanne2016

Word Embeddings for Natural Language Processing

Rémi Philippe Lebret

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Twitter Sentiment Analysis (Almost) from Scratch

Rémi Philippe Lebret, Ronan Collobert, Pedro Henrique Oliveira Pinheiro

A popular application in Natural Language Processing (NLP) is the Sentiment Analysis (SA), i.e., the task of extracting contextual polarity from a given text. The social network Twitter provides an immense amount of text (called tweets) generated by users ...

Idiap2016

"The Sum of Its Parts": Joint Learning of Word and Phrase Representations with Autoencoders

Rémi Philippe Lebret, Ronan Collobert

Recently, there has been a lot of effort to represent words in continuous vector spaces. Those representations have been shown to capture both semantic and syntactic information about words. However, distributed representations of phrases remain a challeng ...

Idiap2015

Linguistic Capitalism and Algorithmic Mediation

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Google’s highly successful business model is based on selling words that appear in search queries. Organizing several million auctions per minute, the company has created the first global linguistic market and demonstrated that linguistic capitalism is a l ...

University of California Press2014

Natural Language Processing (Almost) from Scratch

Ronan Collobert, Michael Karlen

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is a ...

2011

Performance analysis of VP8 image and video compression based on subjective evaluations

Touradj Ebrahimi, Francesca De Simone, Jong Seok Lee, Lutz Goldmann

Today, several alternatives for compression of digital pictures and video sequences exist to choose from. Beside internationally recognized standard solutions, open access options like the VP8 image and video compression have recently appeared and are gain ...

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