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Kelleher, John D. (42)
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Hits 1 – 20 of 42
1
Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification
Nedumpozhimana, Vasudevan
;
Klubička, Filip
;
Kelleher, John D.
In: Front Artif Intell (2022)
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2
Language-Driven Region Pointer Advancement for Controllable Image Captioning ...
Lindh, Annika
;
Ross, Robert J.
;
Kelleher, John D.
. - : arXiv, 2020
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3
Semantic Relatedness and Taxonomic Word Embeddings ...
Kacmajor, Magdalena
;
Kelleher, John D.
;
Klubicka, Filip
. - : arXiv, 2020
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4
English WordNet Taxonomic Random Walk Pseudo-Corpora
Klubicka, Filip
;
Maldonado, Alfredo
;
Mahalunkar, Abhijit
...
In: Conference papers (2020)
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5
Language-Driven Region Pointer Advancement for Controllable Image Captioning
Lindh, Annika
;
Ross, Robert
;
Kelleher, John D.
In: Conference papers (2020)
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6
Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue
Dobnik, Simon
;
Kelleher, John D.
;
Howes, Christine
In: Conference papers (2020)
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7
Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
Klubicka, Filip
;
Mahalunkar, Abhijit
;
Maldonado, Alfredo
...
In: Conference papers (2019)
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8
Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
Maldonado, Alfredo
;
Klubicka, Filip
;
Kelleher, John D.
In: Articles (2019)
Abstract:
Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with taxonomic information from taxonomies like WordNet. This taxonomic enrichment can be done by combining natural-corpus embeddings with taxonomic embeddings (e.g. those trained on a random-walk of WordNet’s structure). This paper conducts a deep analysis of this assumption and shows that both the size of the natural corpus and of the random-walk coverage of the WordNet structure play a crucial role in the performance of combined (enriched) vectors in both similarity tasks. Specifically, we show that embeddings trained on medium-sized natural corpora benefit the most from taxonomic enrichment whilst embeddings trained on large natural corpora only benefit from this enrichment when evaluated on taxonomic similarity tasks. The implication of this is that care has to be taken in controlling the size of the natural corpus and the size of the random-walk used to train vectors. In addition, we find that, whilst the WordNet structure is finite and it is possible to fully traverse it in a single pass, the repetition of well-connected WordNet concepts in extended random-walks effectively reinforces taxonomic relations in the learned embeddings.
Keyword:
Computational Engineering
;
Computational Linguistics
;
retrofitting
;
semantic similarity
;
taxonomic embeddings
;
taxonomic enrichment
;
word embeddings
;
WordNet
URL:
https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1090&context=scschcomart
https://arrow.tudublin.ie/scschcomart/83
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9
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ...
Dobnik, Simon
;
Kelleher, John D.
. - : arXiv, 2018
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10
What is not where: the challenge of integrating spatial representations into deep learning architectures ...
Kelleher, John D.
;
Dobnik, Simon
. - : arXiv, 2018
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11
Is it worth it? Budget-related evaluation metrics for model selection ...
Klubička, Filip
;
Salton, Giancarlo D.
;
Kelleher, John D.
. - : arXiv, 2018
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12
Is it worth it? Budget-related evaluation metrics for model selection
Klubicka, Filip
;
Salton, Giancarlo
;
Kelleher, John D.
In: Conference papers (2018)
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13
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
Dobnik, Simon
;
Ghanimifard, Mehdi
;
Kelleher, John D.
In: Conference papers (2018)
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14
Back to the Future: Logic and Machine Learning
Kelleher, John D.
;
Dobnik, Simon
In: Conference papers (2017)
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15
Robot Perception Errors and Human Resolution Strategies in Situated Human-Robot Dialogue
Kelleher, John D.
;
Mac Namee, Brian
;
Schütte, Niels
In: Articles (2017)
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16
Assessing the Usefulness of Different Feature Sets for Predicting the Comprehension Difficulty of Text
Mac Namee, Brian
;
Kelleher, John D.
;
Fitzpatrick, Noel
In: Conference papers (2017)
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17
Towards a Computational Model of Frame of Reference Alignment in Swedish Dialogue
Dobnik, Simon
;
Howes, Christine
;
Kelleher, John D.
...
In: Conference papers (2016)
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18
A Model for Attention-Driven Judgements in Type Theory with Records
Dobnik, Simon
;
Kelleher, John D.
In: Conference papers (2016)
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19
Fundamentals of Machine Learning for Neural Machine Translation
Kelleher, John D.
In: Conference papers (2016)
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20
Perception Based Misunderstandings in Human-Computer Dialogues
Schütte, Niels
;
Kelleher, John D.
;
Mac Namee, Brian
In: Articles (2014)
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