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Uncertainty sampling and transductive experimental design for active dual supervision

Vikas Sindhwani, Prem Melville, Richard D. Lawrence
2009 Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09  
We apply classical uncertainty and experimental design based active learning schemes to graph/kernel-based dual supervision models.  ...  Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features.  ...  Active Dual Supervision Since GRADS is no different from a standard supervised kernel method with a particular graph-based choice of the kernel over documents and words, many well-developed intuitions  ... 
doi:10.1145/1553374.1553496 dblp:conf/icml/SindhwaniML09 fatcat:iqdvno3c2vcxnnkeutfmkv6dzu

Towards Multi Label Text Classification through Label Propagation

Shweta C, Maya Ingle, Parag Kulkarni
2012 International Journal of Advanced Computer Science and Applications  
Through our paper we are proposing a novel label propagation approach based on semi supervised learning for Multi Label Text Classification.  ...  We are using semi supervised learning technique for effective utilization of labeled and unlabeled data for classification.  ...  multi label text classifier can be improved by using graph based representation of input documents in conjunction with label propagation approach of semi supervised learning [16] [17] .  ... 
doi:10.14569/ijacsa.2012.030607 fatcat:75ae7hgvybdfroyx2gak4ndzci

A Novel Multi label Text Classification Model using Semi supervised learning

Shweta C Dharmadhikari
2012 International Journal of Data Mining & Knowledge Management Process  
Our model is greatly influenced by graph based framework and Semi supervised learning. We demonstrate the effectiveness of our model using Enron , Slashdot , Bibtex and RCV1 datasets.  ...  Our experimental results indicate that the use of Semi Supervised Learning in MLTC greatly improves the decision making capability of classifier.  ...  multi label text classifier can be improved by using graph based representation of input documents and class labels in conjunction with label propagation approach of semi supervised learning [16] [  ... 
doi:10.5121/ijdkp.2012.2402 fatcat:hhn3aa63zjdovnwgbvy25v236a

Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks

He Jiang, Yangqiu Song, Chenguang Wang, Ming Zhang, Yizhou Sun
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
We first decompose the original HIN into several semantically meaningful sub-graphs based the meta-graphs composed of entity and relation types.  ...  In this paper, we present a semi-supervised learning algorithm constrained by the types of HINs.  ...  One of the mainstream semi-supervised learning approaches is the so-called graph based semi-supervised learning [Zhu et al., 2003; Zhou et al., 2003] .  ... 
doi:10.24963/ijcai.2017/270 dblp:conf/ijcai/JiangSWZS17 fatcat:6bcmpzl5lvdtvig3aoejtxjism

Combinatorial and Random Walk Hypergraph Laplacian EigenmapsCombinatorial and Random Walk Hypergraph Laplacian Eigenmaps
Combinatorial and Random Walk Hypergraph Laplacian Eigenmaps

Loc Hoang Tran, Linh Hoang Tran, Hoang Trang, Le Trung Hieu
2015 International Journal of Machine Learning and Computing  
measure of graph based semi-supervised learning method alone (i.e. the baseline method of this paper) applied to the original hypergraph datasets.  ...  Experiment results show that the accuracy performance measures of these two hypergraph Laplacian Eigenmaps combined with graph based semi-supervised learning method are greater than the accuracy performance  ...  learning method 84 88 88 TABLE II : II ACCURACIES OF THE TWO PROPOSED METHODS COMBINED WITH GRAPH BASED SEMI-SUPERVISED LEARNING METHOD AND THE GRAPH BASED SEMI-SUPERVISED LEARNING METHOD ALONE  ... 
doi:10.18178/ijmlc.2015.5.6.553 fatcat:6vccibuxobfyfah34ug7dslota

Analysis of Semi Supervised Learning Methods towards Multi Label Text Classification

S. C.Dharmadhikari, Maya Ingle, Parag Kulkarni
2012 International Journal of Computer Applications  
document datasets , their representation in conjunction with smoothness and manifold assumptions in semi supervised learning may give more relevant classification results.  ...  Supervised methods from machine learning are mainly used for its realization.  ...  Graph-based SSL with multi-label Z. Zha, T. Mie, Z. Wang, X. Hua [11] proposed this algorithm in 2008 . In this work graph based learning framework is proposed.  ... 
doi:10.5120/5775-8026 fatcat:oqkzmctw2zao7hzrgjkt5ut2j4

Self-supervised Document Clustering Based on BERT with Data Augment [article]

Haoxiang Shi, Cen Wang
2021 arXiv   pre-print
In this paper, based on bidirectional encoder representations from transformers, we propose self-supervised contrastive learning (SCL) as well as few-shot contrastive learning (FCL) with unsupervised data  ...  FCL achieves performance close to supervised learning, and FCL with UDA further improves the performance for short texts.  ...  Jing Shen in Beijing University of Posts and Telecommunications (BUPT) for her kind provision of a high performance GPU.  ... 
arXiv:2011.08523v3 fatcat:ggdc23woqvblppjkj6r7s3vz3a

Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

Chang Li, Dan Goldwasser
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents' social context.  ...  We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance  ...  This work was partially supported by a Google Gift.  ... 
doi:10.18653/v1/p19-1247 dblp:conf/acl/LiG19 fatcat:lymkjjdrorhhpnpzto5tbbnjra


Yuxiang Zhang, Yaocheng Chang, Xiaoqing Liu, Sujatha Das Gollapalli, Xiaoli Li, Chunjing Xiao
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
Experimental results demonstrate that our approach signi cantly outperforms the state-of-the-art graph-based keyphrase extraction approaches.  ...  Traditional supervised keyphrase extraction models depend on the features of labelled keyphrases while prevailing unsupervised models mainly rely on structure of the word graph, with candidate words as  ...  ACKNOWLEDGMENTS is work was partially supported by grants from the National Natural Science Foundation of China (Grant No. U1533104, U1633110).  ... 
doi:10.1145/3132847.3132956 dblp:conf/cikm/ZhangCLG0X17 fatcat:rypdj2lo2bbi3eb7tphw67j5yq

Mining top-k Popular Datasets via a Deep Generative Model

Uchenna Akujuobi, Ke Sun, Xiangliang Zhang
2018 2018 IEEE International Conference on Big Data (Big Data)  
By formulating the problem as a semi-supervised multi-label classification one, we develop an efficient deep generative model for learning from both the document content and citation relations.  ...  In this paper, we focus on the problem of extracting top-k popular datasets that have been used in data mining, machine learning, and artificial intelligence fields.  ...  CONCLUSION AND FUTURE WORKS We extended and investigated the use of deep generative models on multi-label graph-based semi-supervised document classification such that it can learn from both the text and  ... 
doi:10.1109/bigdata.2018.8621957 dblp:conf/bigdataconf/Akujuobi0Z18 fatcat:urgvwtoujjggrfh5kj553xxyq4

Machine Learning with World Knowledge: The Position and Survey [article]

Yangqiu Song, Dan Roth
2017 arXiv   pre-print
representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision.  ...  Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn.  ...  of the organizations that supported the work.  ... 
arXiv:1705.02908v1 fatcat:t4fypa6h3vampcp64eosvppsfe

Text Document Clustering and Classification using K-Means Algorithm and Neural Networks

Ramanpreet Kaur, Amandeep Kaur
2016 Indian Journal of Science and Technology  
That is why the main aim of the work is to develop the model based on supervised as well as unsupervised techniques to achieve the similarity between documents.  ...  We developed a model based on supervised as well as unsupervised technique to achieve the similarity between documents.  ...  Simulation Model The objective of the work is to develop a hybrid model that applies the supervised with unsupervised learning techniques for the reduction of gap time consumption of document clustering  ... 
doi:10.17485/ijst/2016/v9i40/97722 fatcat:pansen6pwnf3lddeunpnxr7mhu

Learning to rank with partially-labeled data

Kevin Duh, Katrin Kirchhoff
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems.  ...  Previous work on ranking algorithms has focused on cases where only labeled data is available for training (i.e. supervised learning).  ...  [6] also proposes a graph-based regularization term, but in contrast to [1], it defines the graph nodes not as documents, but as document pairs.  ... 
doi:10.1145/1390334.1390379 dblp:conf/sigir/DuhK08 fatcat:ukqdtmxtuzhu3ebhfr43den2ue

iDVS: An Interactive Multi-document Visual Summarization System [chapter]

Yi Zhang, Dingding Wang, Tao Li
2011 Lecture Notes in Computer Science  
In particular, iDVS uses a new semi-supervised document summarization method to dynamically select sentences based on users' interaction.  ...  To this regard, iDVS tightly integrates semi-supervised learning with interactive visualization for document summarization.  ...  The work is partially supported by US National Science Foundation under grants IIS-0546280 and CCF-0830659.  ... 
doi:10.1007/978-3-642-23808-6_37 fatcat:fpdchtoutrhfxmk6bb3l3n7cwi

Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization

Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, Bo Zhang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
The model jointly learns latent topics and a related classifier with only a small fraction of labeled documents.  ...  As collecting a fully labeled dataset is often time-consuming, semi-supervised learning is of high interest.  ...  Other semi-supervised topic models have been built based on some miscellaneous document supervision, such as the partially-labeled topic assignments [Ramage et al., 2011] or a complex hierarchical topic  ... 
doi:10.24963/ijcai.2017/259 dblp:conf/ijcai/HuZSZZ17 fatcat:xs4t3kasvbdm3ax4jpqips26ha
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