Alexandre Passos
University of Massachusetts Amherst
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Publication
Featured researches published by Alexandre Passos.
empirical methods in natural language processing | 2014
Arvind Neelakantan; Jeevan Shankar; Alexandre Passos; Andrew McCallum
There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale. Nearly all this work, however, assumes a single vector per word type—ignoring polysemy and thus jeopardizing their usefulness for downstream tasks. We present an extension to the Skip-gram model that efficiently learns multiple embeddings per word type. It differs from recent related work by jointly performing word sense discrimination and embedding learning, by non-parametrically estimating the number of senses per word type, and by its efficiency and scalability. We present new state-of-the-art results in the word similarity in context task and demonstrate its scalability by training with one machine on a corpus of nearly 1 billion tokens in less than 6 hours.
conference on computational natural language learning | 2014
Alexandre Passos; Vineet Kumar; Andrew McCallum
Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003---significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data.
meeting of the association for computational linguistics | 2014
Sam Anzaroot; Alexandre Passos; David Belanger; Andrew McCallum
Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference. We extend the technique to perform prediction subject to soft constraints. Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training. This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.
Journal of Machine Learning Research | 2011
Fabian Pedregosa; Gaël Varoquaux; Alexandre Gramfort; Vincent Michel; Bertrand Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron J. Weiss; Vincent Dubourg; Jake Vanderplas; Alexandre Passos; David Cournapeau; Matthieu Brucher; Matthieu Perrot; Edouard Duchesnay
international conference on machine learning | 2012
Alexandre Passos; Piyush Rai; Jacques Wainer; Hal Daumé
neural information processing systems | 2012
David Belanger; Alexandre Passos; Sebastian Riedel; Andrew McCallum
international computer music conference | 2009
Pedro Kröger; Alexandre Passos; Marcos Sampaio
In: (pp. pp. 1844-1852). (2012) | 2012
David Belanger; Alexandre Passos; Sebastian Riedel; Andrew McCallum
Archive | 2011
Alexandre Passos; Hanna M. Wallach; Andrew McCallum
In: (pp. pp. 62-71). (2014) | 2014
David Belanger; Alexandre Passos; Sebastian Riedel; Andrew McCallum