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Hits 1 – 20 of 82
1
An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ...
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
;
Hirst, Graeme
;
Rudzicz, Frank
. - : Underline Science Inc., 2021
BASE
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2
Knowledge Graphs meet Moral Values ...
The 28th International Conference on Computational Linguistics 2020
;
Hirst, Graeme
;
Hulpuș, Ioana
. - : Underline Science Inc., 2020
BASE
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3
Text-based inference of moral sentiment change ...
Xie, Jing Yi
;
Pinto, Renato Ferreira
;
Hirst, Graeme
. - : arXiv, 2020
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4
Computational Analysis of Arguments and Persuasive Strategies in Political Discourse
Naderi, Nona
. - 2020
BASE
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5
Knowledge graphs meet moral values
Hulpus, Ioana
;
Kobbe, Jonathan
;
Stuckenschmidt, Heiner
. - : Association for Computational Linguistics, 2020
BASE
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6
Linguistic fundamentals for natural language processing II: 100 essentials from semantics and pragmatics
Bender, Emily M
;
Lascarides, Alex
;
Hirst, Graeme
. - : Morgan & Claypool Publishers, 2019
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7
Cross-Lingual Sentiment Analysis Without (Good) Translation ...
Abdalla, Mohamed
;
Hirst, Graeme
. - : arXiv, 2017
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8
Exploiting Linguistic Knowledge in Lexical and Compositional Semantic Models
Wang, Tong
. - 2017
Abstract:
A fundamental principle in distributional semantic models is to use similarity in linguistic environment as a proxy for similarity in meaning. Known as the distributional hypothesis, the principle has been successfully applied to many areas in natural language processing. As a proxy, however, it also suffers from critical limitations and exceptions, many of which are the result of the overly simplistic definitions of the linguistic environment. In this thesis, I hypothesize that the quality of distributional models can be improved by carefully incorporating linguistic knowledge into the definition of linguistic environment. The hypothesis is validated in the context of three closely related semantic relations, namely near-synonymy, lexical similarity, and polysemy. On the lexical level, the definition of linguistic environment is examined under three different distributional frameworks including lexical co-occurrence, syntactic and lexicographic dependency, and taxonomic structures. Firstly, combining kernel methods and lexical level co-occurrence with matrix factorization is shown to be highly effective in capturing the fine-grained nuances among near-synonyms. Secondly, syntactic and lexicographic information is shown to result in notable improvement in lexical embedding learning when evaluated in lexical similarity benchmarks. Thirdly, for taxonomy-based measures of lexical similarity, the intuitions for using structural features such as depth and density are examined and challenged, and the refined definitions are shown to improve correlation between the features and human judgements of similarity as well as performances of similarity measures using these features. On the compositional level, distributional models of multi-word contexts are also shown to benefit from incorporating syntactic and lexicographic knowledge. Analytically, the use of syntactic teacher-forcing is motivated by derivations of full gradients in long short-term memory units in recurrent neural networks. Empirically, syntactic knowledge helps achieve statistically significant improvement in language modelling and state-of-the-art accuracy in near-synonym lexical choice. Finally, a compositional similarity function is developed to measure the similarity between two sets of random events. Application in polysemy with lexicographic knowledge produces state-of-the-art performance in unsupervised word sense disambiguation. ; Ph.D.
Keyword:
0984
;
Artificial Intelligence
;
Compositional Semantics
;
Computational Linguistics
;
Distributional Semantics
;
Lexical Semantics
URL:
http://hdl.handle.net/1807/77437
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9
Automatic Text and Speech Processing for the Detection of Dementia
Fraser, Kathleen
. - 2017
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10
Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments (Dagstuhl Seminar 16161)
Cabrio, Elena
;
Hirst, Graeme
;
Villata, Serena
. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2016. : Dagstuhl Reports. Dagstuhl Reports, Volume 6, Issue 4, 2016
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11
Resolving Shell Nouns
Kolhatkar, Varada
. - 2015
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12
RST-style Discourse Parsing and Its Applications in Discourse Analysis
Feng, Vanessa Wei
. - 2015
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13
Patterns of local discourse coherence as a feature for authorship attribution
Feng, Vanessa Wei
;
Hirst, Graeme
In:
LLC. - Oxford : Oxford Univ. Press
29 (2014) 2, 191
OLC Linguistik
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14
Computational Approaches to Style and the Lexicon
Brooke, Julian
. - 2014
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15
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts ; Automated classification of primary progressive aphasia subtypes from narrative speech samples
Rochon, Elizabeth
;
Fraser, Kathleen C
;
Graham, Naida L
. - : Elsevier, 2014
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16
Computational linguistics
Hirst, Graeme
In:
The Oxford handbook of the history of linguistics
(Oxford, 2013), p. 707-726
MPI für Psycholinguistik
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17
Computing Lexical Contrast ...
Mohammad, Saif M.
;
Dorr, Bonnie J.
;
Hirst, Graeme
. - : arXiv, 2013
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18
Automated methods for text correction
Rozovskaya, Alla
. - 2013
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19
Changes in Style in Authors with Alzheimer's Disease
Hirst, Graeme
;
Wei Feng, Vanessa
In:
English studies. - Abingdon : Routledge, Taylor & Francis Group
93 (2012) 3, 357-370
OLC Linguistik
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20
TORGO Database of Dysarthric Articulation
Rudzicz, Frank
;
Hirst, Graeme
;
van Lieshout, Pascal
. - : Linguistic Data Consortium, 2012. : https://www.ldc.upenn.edu, 2012
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