Paul Nulty


2021

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Can Domain Pre-training Help Interdisciplinary Researchers from Data Annotation Poverty? A Case Study of Legal Argument Mining with BERT-based Transformers
Gechuan Zhang | David Lillis | Paul Nulty
Proceedings of the Workshop on Natural Language Processing for Digital Humanities

Interdisciplinary Natural Language Processing (NLP) research traditionally suffers from the requirement for costly data annotation. However, transformer frameworks with pre-training have shown their ability on many downstream tasks including digital humanities tasks with limited small datasets. Considering the fact that many digital humanities fields (e.g. law) feature an abundance of non-annotated textual resources, and the recent achievements led by transformer models, we pay special attention to whether domain pre-training will enhance transformer’s performance on interdisciplinary tasks and how. In this work, we use legal argument mining as our case study. This aims to automatically identify text segments with particular linguistic structures (i.e., arguments) from legal documents and to predict the reasoning relations between marked arguments. Our work includes a broad survey of a wide range of BERT variants with different pre-training strategies. Our case study focuses on: the comparison of general pre-training and domain pre-training; the generalisability of different domain pre-trained transformers; and the potential of merging general pre-training with domain pre-training. We also achieve better results than the current transformer baseline in legal argument mining.

2020

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The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances
Paul Nulty | David Lillis
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the UCD system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an implementation of a web application for the purpose of searching and visualising semantic networks. The results of the change measure used for this task were not among the best performing systems, but further calibration of the distance metric and backoff approaches may improve this method.

2017

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Network Visualisations for Exploring Political Concepts
Paul Nulty
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Short papers

2010

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UCD-PN: Selecting General Paraphrases Using Conditional Probability
Paul Nulty | Fintan Costello
Proceedings of the 5th International Workshop on Semantic Evaluation

2009

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Using Lexical Patterns in the Google Web 1T Corpus to Deduce Semantic Relations Between Nouns
Paul Nulty | Fintan Costello
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

2007

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Semantic Classification of Noun Phrases Using Web Counts and Learning Algorithms
Paul Nulty
Proceedings of the ACL 2007 Student Research Workshop

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UCD-PN: Classification of Semantic Relations Between Nominals using WordNet and Web Counts
Paul Nulty
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)