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Simple Search
Hits 1 – 13 of 13
1
Autoencoding Improves Pre-trained Word Embeddings ...
Kaneko, Masahiro
;
Bollegala, Danushka
. - : arXiv, 2020
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2
Autoencoding Improves Pre-trained Word Embeddings ...
The 28th International Conference on Computational Linguistics 2020
;
Bollegala, Danushka
;
Kaneko, Masahiro
. - : Underline Science Inc., 2020
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3
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction ...
The 28th International Conference on Computational Linguistics 2020
;
Bollegala, Danushka
;
Coenen, Frans
. - : Underline Science Inc., 2020
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4
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ...
Bollegala, Danushka
;
Kiryo, Ryuichi
;
Tsujino, Kosuke
;
Yukawa, Haruki
. - : arXiv, 2020
Abstract:
Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce ... : To appear in the 12th Language Resources and Evaluation (LREC 2020) Conference ...
Keyword:
Artificial Intelligence cs.AI
;
Computation and Language cs.CL
;
FOS Computer and information sciences
;
Machine Learning cs.LG
URL:
https://arxiv.org/abs/2002.11004
https://dx.doi.org/10.48550/arxiv.2002.11004
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5
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction.
Mandya, Angrosh
;
Coenen, Frans
;
Bollegala, Danushka
. - : International Committee on Computational Linguistics, 2020
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6
Multi-Source Attention for Unsupervised Domain Adaptation.
Bollegala, Danushka
;
Cui, Xia
. - : Association for Computational Linguistics, 2020
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7
Learning to Compose Relational Embeddings in Knowledge Graphs
Hakami, Huda
;
Chen, Wenye
;
Bollegala, Danushka
. - : Springer Singapore, 2020
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8
Tree-Structured Neural Topic Model
Isonuma, Masaru
;
Mori, Junichiro
;
Bollegala, Danushka
. - 2020
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9
A Pilot Study on Argument Simplification in Stance-Based Opinions
Bollegala, Danushka
;
Rajendran, Pavithra
;
Parsons, Simon
. - : Springer Singapore, 2020
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10
Joint Approaches for Learning Word Representations from Text Corpora and Knowledge Bases
Alsuhaibani, Mohammed
. - 2020
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11
Autoencoding Improves Pre-trained Word Embeddings.
Kaneko, Masahiro
;
Bollegala, Danushka
. - : International Committee on Computational Linguistics, 2020
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12
Context-Guided Self-supervised Relation Embeddings
Hakami, Huda
;
Bollegala, Danushka
. - : Springer Singapore, 2020
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13
A Study on Learning Representations for Relations Between Words
Hakami, Huda
. - 2020
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