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1
Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification
In: Front Artif Intell (2022)
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2
Language-Driven Region Pointer Advancement for Controllable Image Captioning ...
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3
Semantic Relatedness and Taxonomic Word Embeddings ...
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4
English WordNet Taxonomic Random Walk Pseudo-Corpora
In: Conference papers (2020)
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5
Language-Driven Region Pointer Advancement for Controllable Image Captioning
In: Conference papers (2020)
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6
Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue
In: Conference papers (2020)
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7
Capturing and measuring thematic relatedness [<Journal>]
DNB Subject Category Language
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8
Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
In: Conference papers (2019)
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9
Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
In: Articles (2019)
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10
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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11
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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12
Is it worth it? Budget-related evaluation metrics for model selection ...
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13
Is it worth it? Budget-related evaluation metrics for model selection
In: Conference papers (2018)
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14
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
In: Conference papers (2018)
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15
Back to the Future: Logic and Machine Learning
In: Conference papers (2017)
Abstract: In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.
Keyword: computational linguistic; Computational Linguistics; Computer Sciences; data driven approaches; deep learning; dialogue; formal approaches; language technology; logic; machine learning; spatial language; structure learning
URL: https://arrow.tudublin.ie/airccon/9
https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1009&context=airccon
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16
Robot Perception Errors and Human Resolution Strategies in Situated Human-Robot Dialogue
In: Articles (2017)
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17
Assessing the Usefulness of Different Feature Sets for Predicting the Comprehension Difficulty of Text
In: Conference papers (2017)
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18
Towards a Computational Model of Frame of Reference Alignment in Swedish Dialogue
In: Conference papers (2016)
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19
A Model for Attention-Driven Judgements in Type Theory with Records
In: Conference papers (2016)
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20
Fundamentals of Machine Learning for Neural Machine Translation
In: Conference papers (2016)
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