Brian Murphy


2020

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Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment
Liam Watson | Anna Jurek-Loughrey | Barry Devereux | Brian Murphy
Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources

While there is a rich literature on the tracking of sentiment and emotion in texts, modelling the emotional trajectory of longer narratives, such as literary texts, poses new challenges. Previous work in the area of sentiment analysis has focused on using information from within a sentence to predict a valence value for that sentence. We propose to explore the influence of previous sentences on the sentiment of a given sentence. In particular, we investigate whether information present in a history of previous sentences can be used to predict a valence value for the following sentence. We explored both linear and non-linear models applied with a range of different feature combinations. We also looked at different context history sizes to determine what range of previous sentence context was the most informative for our models. We establish a linear relationship between sentence context history and the valence value of the current sentence and demonstrate that sentences in closer proximity to the target sentence are more informative. We show that the inclusion of semantic word embeddings further enriches our model predictions.

2018

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Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge
Steven Derby | Paul Miller | Brian Murphy | Barry Devereux
Proceedings of the 22nd Conference on Computational Natural Language Learning

Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms. However, the dimensions of dense embedding spaces are not designed to resemble human semantic knowledge. Moreover, embeddings are often built from a single source of information (typically text data), even though neurocognitive research suggests that semantics is deeply linked to both language and perception. In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding. Through in-depth analyses comparing these sparse models to human-derived behavioural and neuroimaging data, we demonstrate their ability to predict interpretable linguistic descriptions of human ground-truth semantic knowledge.

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Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model
Steven Derby | Paul Miller | Brian Murphy | Barry Devereux
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Performance in language modelling has been significantly improved by training recurrent neural networks on large corpora. This progress has come at the cost of interpretability and an understanding of how these architectures function, making principled development of better language models more difficult. We look inside a state-of-the-art neural language model to analyse how this model represents high-level lexico-semantic information. In particular, we investigate how the model represents words by extracting activation patterns where they occur in the text, and compare these representations directly to human semantic knowledge.

2016

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BrainBench: A Brain-Image Test Suite for Distributional Semantic Models
Haoyan Xu | Brian Murphy | Alona Fyshe
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning
Anna Korhonen | Alessandro Lenci | Brian Murphy | Thierry Poibeau | Aline Villavicencio
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning

2015

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Evidence of syntactic working memory usage in MEG data
Marten van Schijndel | Brian Murphy | William Schuler
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

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A Compositional and Interpretable Semantic Space
Alona Fyshe | Leila Wehbe | Partha P. Talukdar | Brian Murphy | Tom M. Mitchell
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning
Alona Fyshe | Partha P. Talukdar | Brian Murphy | Tom M. Mitchell
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition
Alona Fyshe | Brian Murphy | Partha Talukdar | Tom Mitchell
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

2012

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Selecting Corpus-Semantic Models for Neurolinguistic Decoding
Brian Murphy | Partha Talukdar | Tom Mitchell
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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On discriminating fMRI representations of abstract WordNet taxonomic categories
Andrew Anderson | Tao Yuan | Brian Murphy | Massimo Poesio
Proceedings of the 3rd Workshop on Cognitive Aspects of the Lexicon

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Modeling Word Meaning: Distributional Semantics and the Corpus Quality-Quantity Trade-Off
Seshadri Sridharan | Brian Murphy
Proceedings of the 3rd Workshop on Cognitive Aspects of the Lexicon

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Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding
Brian Murphy | Partha Talukdar | Tom Mitchell
Proceedings of COLING 2012

2011

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PaddyWaC: A Minimally-Supervised Web-Corpus of Hiberno-English
Brian Murphy | Egon W. Stemle
Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties

2010

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Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Brian Murphy | Kai-min Kevin Chang | Anna Korhonen
Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics

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Detecting Semantic Category in Simultaneous EEG/MEG Recordings
Brian Murphy | Massimo Poesio
Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics

2009

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EEG responds to conceptual stimuli and corpus semantics
Brian Murphy | Marco Baroni | Massimo Poesio
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing