Martin Emms


2016

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Dynamic Generative model for Diachronic Sense Emergence Detection
Martin Emms | Arun Kumar Jayapal
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

As time passes words can acquire meanings they did not previously have, such as the ‘twitter post’ usage of ‘tweet’. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the ‘pseudo-word’ technique (Schutze, 1992) to give a novel further evaluation via ‘pseudo-neologisms’. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.

2015

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An unsupervised EM method to infer time variation in sense probabilities
Martin Emms | Arun Jayapal
Proceedings of the 12th International Conference on Natural Language Processing

2014

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TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach
Arun Kumar Jayapal | Martin Emms | John Kelleher
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Detecting change and emergence for multiword expressions
Martin Emms | Arun Jayapal
Proceedings of the 10th Workshop on Multiword Expressions (MWE)

2011

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Measuring the Compositionality of Collocations via Word Co-occurrence Vectors: Shared Task System Description
Alfredo Maldonado-Guerra | Martin Emms
Proceedings of the Workshop on Distributional Semantics and Compositionality

2009

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Exploring Multilingual Semantic Role Labeling
Baoli Li | Martin Emms | Saturnino Luz | Carl Vogel
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

2008

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Tree Distance and Some Other Variants of Evalb
Martin Emms
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Some alternatives to the standard evalb measures for parser evaluation are considered, principally the use of a tree-distance measure, which assigns a score to a linearity and ancestry respecting mapping between trees, in contrast to the evalb measures, which assign a score to a span preserving mapping. Additionally, analysis of the evalb measures suggests some further variants, concerning different normalisations, the portions of a tree compared and whether scores should be micro or macro averaged. The outputs of 6 parsing systems on Section 23 of the Penn Treebank were taken. It is shown that the ranking of the parsing systems varies as the alternative evaluation measures are used. For a fixed parsing system, it is also shown that the ranking of the parses from best to worst will vary according to whether the evalb or tree-distance measure is used. It is argued that the tree-distance measure ameliorates a problem that has been noted concerning over-penalisation of attachment errors.

2006

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Variants of Tree Similarity in a Question Answering Task
Martin Emms
Proceedings of the Workshop on Linguistic Distances

1993

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Parsing with polymorphism
Martin Emms
Sixth Conference of the European Chapter of the Association for Computational Linguistics