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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 (2)
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Simple Search
Hits 1 – 11 of 11
1
How does Attention Affect the Model? ...
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
;
Hua, Lingyu
;
Li, Qiuchi
. - : Underline Science Inc., 2021
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2
Exploiting Position Bias for Robust Aspect Sentiment Classification ...
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
;
Ma, Fang
;
Song, Dawei
. - : Underline Science Inc., 2021
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3
A Generalized Language Model in Tensor Space ...
Zhang, Lipeng
;
Zhang, Peng
;
Ma, Xindian
. - : arXiv, 2019
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4
A quantum-inspired sentiment representation model for twitter sentiment analysis
Zhang, Yazhou
;
Song, Dawei
;
Zhang, Peng
. - 2019
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5
Quantum-inspired Complex Word Embedding
Uprety, Sagar
;
Li, Qiuchi
;
Song, Dawei
. - : Association for Computational Linguistics, 2018
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6
Characterizing pure high-order entanglements in lexical semantic spaces via information geometry
Hou, Yuexian
;
Song, Dawei
. - 2009
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7
Towards operational abduction from a cognitive perspective
Bruza, Peter
;
Cole, Richard
;
Song, Dawei
. - 2006
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8
Information flow analysis with Chinese text
Cheong, Paulo
;
Song, Dawei
;
Bruza, Peter
. - : Springer Verlag, 2005
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9
A Correlation Analysis on LSA and HAL Semantic Space Models
Yan, Xin
;
Li, Xue
;
Song, Dawei
. - 2004
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10
Fuzzy K-means clustering on a high dimensional semantic space
Cao, Guihong
;
Song, Dawei
;
Bruza, Peter
. - : Springer Verlag, 2004
Abstract:
One way of representing semantics is via a high dimensional conceptual space constructed from lexical co-occurrence. Concepts (words) are represented as a vector whereby the dimensions are other words. As the words are represented as dimensional objects, clustering techniques can be applied to compute word clusters. Conventional clustering algorithms, e.g., the K-means method, however, normally produce crisp clusters, i.e., an object is assigned to only one cluster. This is sometimes not desirable. Therefore, a fuzzy membership function can be applied to the K-Means clustering, which models the degree of an object belonging to certain cluster. This paper introduces a fuzzy k-means clustering algorithm and how it is used to word clustering on the high dimensional semantic space constructed by a cognitively motivated semantic space model, namely Hyperspace Analogue to Language. A case study demonstrates the method is promising.
Keyword:
1700 Computer Science
;
2614 Theoretical Computer Science
URL:
https://espace.library.uq.edu.au/view/UQ:5605a13
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11
Towards context sensitive information inference
Song, Dawei
;
Bruza, Peter D.
In:
American Society for Information Science and Technology. Journal of the American Society for Information Science and Technology. - New York, NY : Wiley
54 (2003) 4, 321-334
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