TextCSN: A semi-supervised approach for text clustering using pairwise constraints and convolutional siamese network
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The 35th Annual ACM Symposium on Applied Computing: Brno, Czech Republic, March 30 - April 3, 2020. New York: Association for Computing Machinery, Inc, 2020. S. 1135-1142.
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - TextCSN
T2 - Annual ACM Symposium on Applied Computing - SAC 2020
AU - Vilhagra, Lucas Akayama
AU - Fernandes, Eraldo Rezende
AU - Nogueira, Bruno Magalhães
N1 - Conference code: 35
PY - 2020/3/30
Y1 - 2020/3/30
N2 - Clustering is a key problem in several applications. Although this task is originally unsupervised, there are many proposals leveraging different supervision signals in order to improve clustering performance. Some semi-supervised clustering methods employ pairwise constraints to inform the learning algorithm about pairs of instances that should be in the same cluster (must-link constraints or similar instances) and pairs that should be in different clusters (cannot-link constraints or dissimilar instances). In many applications, to provide pairwise constraints is cheaper than asking users for explicit labels on the data. More recently, deep clustering methods have been proposed in the literature. Such methods consists in learning a deep neural representation of the input data in order to improve clustering. In this paper, we present TextCSN, a deep clustering approach that combines (i) a Convolutional Siamese Network (CSN) based on pairwise constraints to perform representation learning and (ii) the traditional K-Means algorithm for unsupervised clustering using the learned representation. As far as we know, this is the first semi-supervised deep learning method based on pairwise constraints applied on text clustering. By means of eight text clustering tasks, we assess our approach comparing it with two baselines: MPC-KMeans, a semi-supervised clustering algorithm; and ordinary K-Means algorithm. Results indicate that the proposed approach outperforms the baselines in six of these datasets, and its performance increases with the number of constraints provided.
AB - Clustering is a key problem in several applications. Although this task is originally unsupervised, there are many proposals leveraging different supervision signals in order to improve clustering performance. Some semi-supervised clustering methods employ pairwise constraints to inform the learning algorithm about pairs of instances that should be in the same cluster (must-link constraints or similar instances) and pairs that should be in different clusters (cannot-link constraints or dissimilar instances). In many applications, to provide pairwise constraints is cheaper than asking users for explicit labels on the data. More recently, deep clustering methods have been proposed in the literature. Such methods consists in learning a deep neural representation of the input data in order to improve clustering. In this paper, we present TextCSN, a deep clustering approach that combines (i) a Convolutional Siamese Network (CSN) based on pairwise constraints to perform representation learning and (ii) the traditional K-Means algorithm for unsupervised clustering using the learned representation. As far as we know, this is the first semi-supervised deep learning method based on pairwise constraints applied on text clustering. By means of eight text clustering tasks, we assess our approach comparing it with two baselines: MPC-KMeans, a semi-supervised clustering algorithm; and ordinary K-Means algorithm. Results indicate that the proposed approach outperforms the baselines in six of these datasets, and its performance increases with the number of constraints provided.
KW - Deep clustering
KW - Neural networks
KW - Representation learning
KW - Semi-supervised clustering
KW - Text clustering
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85083023584&partnerID=8YFLogxK
U2 - 10.1145/3341105.3374018
DO - 10.1145/3341105.3374018
M3 - Article in conference proceedings
AN - SCOPUS:85083023584
SP - 1135
EP - 1142
BT - The 35th Annual ACM Symposium on Applied Computing
PB - Association for Computing Machinery, Inc
CY - New York
Y2 - 30 March 2020 through 3 April 2020
ER -