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Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

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Building Word Embeddings for Solving Natural Language Processing

Rémi Philippe Lebret

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

EPFL2016

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Pronoun Translation and Prediction with or without Coreference Links

Andrei Popescu-Belis, Lesly Sadiht Miculicich Werlen

The Idiap NLP Group has participated in both DiscoMT 2015 sub-tasks: pronoun-focused translation and pronoun prediction. The system for the first sub-task combines two knowledge sources: gram matical constraints from the hypothesized coreference links, and ...

2015

On Actively Teaching the Crowd to Classify

Is it possible to teach workers while crowdsourcing classification tasks? Amongst the challenges: (a) workers have different (unknown) skills, competence, and learning rate to which the teaching must be adapted, (b) feedback on the workers’ progress is lim ...

2013

Quality-aware similarity assessment for entity matching in Web data

Karl Aberer, Zoltán Miklós, Surender Reddy Yerva

One of the key challenges to realize automated processing of the information on the Web, which is the central goal of the Semantic Web, is related to the entity matching problem. There are a number of tools that reliably recognize named entities, such as p ...

Wiley-Blackwell2012

TweetSpector: Entity-based retrieval of Tweets

Karl Aberer, Zoltán Miklós, Surender Reddy Yerva, Flavia Adelina Grosan, Alexandru Octavian Tandrau

People readily express their opinions about the various products, companies, TV shows etc., on Twitter. These tweet messages are thus a rich source of information that can be exploited to understand the sentiments about the concerned products or services. ...

2012

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

idMesh: graph-based disambiguation of linked data

Karl Aberer, Philippe Cudré-Mauroux, Parisa Haghani, Michael Jost

We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities -- represented by globally identifiable declarative artifacts -- self-organize in a dynamic and probabilistic manner. Our solution has the ...

ACM2009