Will Roberts


2019

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Annotation and Automatic Classification of Aspectual Categories
Markus Egg | Helena Prepens | Will Roberts
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present the first annotated resource for the aspectual classification of German verb tokens in their clausal context. We use aspectual features compatible with the plurality of aspectual classifications in previous work and treat aspectual ambiguity systematically. We evaluate our corpus by using it to train supervised classifiers to automatically assign aspectual categories to verbs in context, permitting favourable comparisons to previous work.

2018

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A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality
Will Roberts | Markus Egg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Proceedings of the ACL 2016 Student Research Workshop
He He | Tao Lei | Will Roberts
Proceedings of the ACL 2016 Student Research Workshop

2014

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A Comparison of Selectional Preference Models for Automatic Verb Classification
Will Roberts | Markus Egg
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Subcategorisation Acquisition from Raw Text for a Free Word-Order Language
Will Roberts | Markus Egg | Valia Kordoni
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2012

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Using Verb Subcategorization for Word Sense Disambiguation
Will Roberts | Valia Kordoni
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We develop a model for predicting verb sense from subcategorization information and integrate it into SSI-Dijkstra, a wide-coverage knowledge-based WSD algorithm. Adding syntactic knowledge in this way should correct the current poor performance of WSD systems on verbs. This paper also presents, for the first time, an evaluation of SSI-Dijkstra on a standard data set which enables a comparison of this algorithm with other knowledge-based WSD systems. Our results show that our system is competitive with current graph-based WSD algorithms, and that the subcategorization model can be used to achieve better verb sense disambiguation performance.