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Hits 1 – 20 of 51
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
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
Bhardwaj, Peru
;
Kelleher, John
;
Costabello, Luca
. - : arXiv, 2021
BASE
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3
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bhardwaj, Peru
;
Costabello, Luca
. - : Underline Science Inc., 2021
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4
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Bhardwaj, Peru
;
Costabello, Luca
. - : Underline Science Inc., 2021
BASE
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5
Style versus Content: A distinction without a (learnable) difference?
Jafaritazehjani, Somayeh
;
Lecorvé, Gwénolé
;
Lolive, Damien
...
In: International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03112354 ; International Conference on Computational Linguistics, Dec 2020, Virtual, Spain (2020)
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6
Language-Driven Region Pointer Advancement for Controllable Image Captioning ...
Lindh, Annika
;
Ross, Robert J.
;
Kelleher, John D.
. - : arXiv, 2020
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7
Semantic Relatedness and Taxonomic Word Embeddings ...
Kacmajor, Magdalena
;
Kelleher, John D.
;
Klubicka, Filip
. - : arXiv, 2020
BASE
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8
English WordNet Taxonomic Random Walk Pseudo-Corpora
Klubicka, Filip
;
Maldonado, Alfredo
;
Mahalunkar, Abhijit
...
In: Conference papers (2020)
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9
Language-Driven Region Pointer Advancement for Controllable Image Captioning
Lindh, Annika
;
Ross, Robert
;
Kelleher, John D.
In: Conference papers (2020)
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10
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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11
Capturing and measuring thematic relatedness [<Journal>]
Kacmajor, Magdalena Verfasser]
DNB Subject Category Language
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12
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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13
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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14
TEST: A terminology extraction system for technology related terms
Hossari, Murhaf
;
Dev, Soumyabrata
;
Kelleher, John
In: Conference papers (2019)
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15
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ...
Dobnik, Simon
;
Kelleher, John D.
. - : arXiv, 2018
BASE
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16
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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17
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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18
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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19
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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20
Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier ...
Kelleher, John; Ross, Robert J. And Salton, Giancarlo
. - : Dublin Institute of Technology, 2017
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