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Using Virtual Examples for Text Classification with Support Vector Machines

2006 Journal of Natural Language Processing  
We explore how virtual examples(artificially created examples)improve performance of text classification with Support Vector Machines(SVMs).We propose techniques to create virtual examples for text classification  ...  We evaluate the proposed methods by Reuters-21758 test set collection.Experimental results show virtual examples improve the performance of text classification with SVMs,especially for small training sets  ...  Text Categorization with Support Vector Machines: Learning with Many Relevant Features." In Proceedings of the 10th European Conference on Machine Learning, pp.137-142. Joachims, T.(1999)."  ... 
doi:10.5715/jnlp.13.3_21 fatcat:zibv2c2xujgrfiemzpukdenvzi

TREC 11 Experiments at NII: The Effects of Virtual Relevant Documents in Batch Filtering

Kyung-Soon Lee, Kyo Kageura, Akiko N. Aizawa
2002 Text Retrieval Conference  
Researches on document retrieval, text categorization and routing have shown the effects of learning by sampling relevant documents or non-relevant document from training set.  ...  Support Vectors Given training documents which include not only training documents but also VRDs, we have used support vector machine (Vapnik, 1995) Experiments Experimental Procedure In the runs  ... 
dblp:conf/trec/LeeKA02 fatcat:rlw5wvpsgncuja5rqh5l4dp6ry

A multilingual text mining approach to web cross-lingual text retrieval

Rowena Chau, Chung-Hsing Yeh
2004 Knowledge-Based Systems  
Second, the multilingual concept -term relationships, in turn, are used to discover the conceptual content of the multilingual text, which is either a document containing potentially relevant information  ...  data relevant to a domain.  ...  In our multilingual text categorization problem, the nearest neighbor to an unclassified text with k index terms will be the k single-term virtual documents where each of them contains one of the unclassified  ... 
doi:10.1016/j.knosys.2004.04.001 fatcat:xcvrtmnhkve67eauw6ohzjpky4

Construction of supervised and unsupervised learning systems for multilingual text categorization

Chung-Hong Lee, Hsin-Chang Yang
2009 Expert systems with applications  
We selected support vector machines (SVM) as representative of supervised techniques as well as latent semantic indexing (LSI) and self-organizing maps (SOM) techniques as our selective ones of unsupervised  ...  relevant information, in whatever language and form it may have been stored.  ...  Implementation of supervised methods for MTC In our previous work, we employed a supervised text mining technique based on support vector machines (SVMs) for training text classifiers in a combined platform  ... 
doi:10.1016/j.eswa.2007.12.052 fatcat:rwqnbwz2lra33o4nwvpge6peqi

NewsFinder: Automating an AI News Service

Joshua Eckroth, Liang Dong, Reid G. Smith, Bruce G. Buchanan
2012 The AI Magazine  
NewsFinder automates the steps involved in finding, selecting, categorizing, and publishing news stories that meet relevance criteria for the Artificial Intelligence community.  ...  The software combines a broad search of online news sources with topic-specific trained models and heuristics.  ...  Support Vector Machines for Categorization The task of choosing one or more categories for a news story is known as multilabel classification.  ... 
doi:10.1609/aimag.v33i2.2406 fatcat:ljpvypr6lvg33nuomja6k7zgyq

Empirical Evaluations of Automatic Forum Selector

Chen-Huei Chou
2012 International Journal of Computer and Communication Engineering  
In this study, we propose the use of text categorization approach to automatically select a target forum category. The empirical evaluations demonstrate the utility of text categorization approach.  ...  We also found that decision tree outperformed other machine classifiers.  ...  Support Vector Machine consistently improved its performance when more top ranked attributes were added.  ... 
doi:10.7763/ijcce.2012.v1.16 fatcat:b74drzjo3bclfe5r6zgysxgu6q

Sentence Classification using Machine Learning with Term Frequency–Inverse Document Frequency with N-Gram [chapter]

Nagendra Nagaraj, Department of Computer Science, Christ University, Bangalore, India, Chandra J
2021 New Frontiers in Communication and Intelligent Systems  
The linear support vector machine is most relevant to this work with our proposed model. The final result shows a significant accuracy compared with earlier methods.  ...  This paper demonstrates machine learning techniques are used for the text classification process..And also, with the vast rapid growth of text analysis in all areas, the demand for automatic text classification  ...  A support vector machine paired with unigrams delivered the best result in their experiments.  ... 
doi:10.52458/978-81-95502-00-4-35 fatcat:rndrxg6tqncddfscshifzogl24

Machine Learning Algorithms in Web Page Classification

A. Rama, B. Nagalakshmi
2015 Indian Journal of Science and Technology  
In this paper we tend to use machine learning algorithms like SVM, KNN and GIS to perform a behavior comparison on the net pages classifications drawback, from the experiment we tend to see within the  ...  SVM with tiny range of negative documents to make the centroids has the littlest storage demand and also the least on line take a look at computation value.  ...  Machine Learning Algorithms Support Vector Machines (SVM) Support Vector Machine is Associate in Nursing machine learning technique supported applied math Learning theory.  ... 
doi:10.17485/ijst/2015/v8i31/88974 fatcat:t4ykcxxqrzarnlyimrlyocaxhm

Page 242 of American Society of Civil Engineers. Collected Journals Vol. 16, Issue 4 [page]

2002 American Society of Civil Engineers. Collected Journals  
Text categorization with support vector machines: learning with many relevant features.” Proc., ECML-98, Springer, Berlin, 137-142. Joachims, T. (1999). “Making large-scale SVM learning practical.”’  ...  “A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization.” Proc., ICML-97, Morgan Kauf- mann, San Francisco, 143-151. Joachims, T. (1998).  ... 

Fusion Approaches for Mappings between Heterogeneous Ontologies [chapter]

Thomas Mandl, Christa Womser-Hacker
2001 Lecture Notes in Computer Science  
Text categorization has discussed learning methods to map between full text terms and thesaurus descriptors.  ...  Automatic methods for mappings between different ontologies are necessary to ensure successful retrieval of information stored in virtual digital libraries.  ...  In recent years neural networks and support vector machines have been employed as well [8, 11] . An overview is provided by Aas and Eikvil [1] .  ... 
doi:10.1007/3-540-44796-2_8 fatcat:g4ln6lxoa5brhncp6mrvtl7ngu

Novel top-down methods for Hierarchical Text Classification

Cao Ying, Duan run-ying
2011 Procedia Engineering  
We define an virtual subclass for each non-leaf category to help the rejected documents go back to ancestor category ,thus improving the overall performance .Our experiments using Support Vector Machine  ...  To classify large-scale text corpora, one common approach is using hierarchical text classification and classifying text documents in a top-down manner.  ...  In the literature, many algorithms [1] have been proposed, such as Support Vector Machines (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB) and so on.  ... 
doi:10.1016/j.proeng.2011.11.2651 fatcat:yhn4izj3zvfezjf4xbu2zdu4tu

DACS Dewey index-based Arabic Document Categorization System

A. F.Alajmi, E. M Saad, M H Awadalla
2012 International Journal of Computer Applications  
Finally a Dewey-Index Based Back-propagation Artificial Neural Network is developed for Arabic Document Categorization.  ...  This paper is devoted to the development of Arabic Text Categorization System. First, a stop-words list is generated using statistical approach which captures the inflation of different Arabic words.  ...  Text processing tasks: The main goal of this research is to develop a categorization system that start with row text data and ends up with a tagged document with relevant classes.  ... 
doi:10.5120/7500-0634 fatcat:4fvnz7yqyrhkxb2vfrgksm7324

Optimizing Support Vector Machine Classification Based on Semantic-Text Knowledge Enrichment

Mr. Shadi Diab, Mr. Nasim Hamaydeh
2019 Zenodo  
In this research, we enhanced the performance of Support Vector Machine (SVM) in text classification by applying semantic-knowledge enrichment.  ...  We propose using semantic-knowledge enrichment scheme to inject new concepts into the original contents of the text documents.  ...  Huang, «Support Vector Machines for Text Categorization Based on Latent Semantic Indexing,» 2001. 24.  ... 
doi:10.5281/zenodo.2576351 fatcat:cvvpr7aeeve6lhkah6uebh5aka

Two Hierarchical Text Categorization Approaches for BioASQ Semantic Indexing Challenge

Francisco J. Ribadas-Pena, Luis M. de Campos Ibañez, Víctor Manuel Darriba Bilbao, Alfonso E. Romero
2013 Conference and Labs of the Evaluation Forum  
This paper describes our participation in the BioASQ semantic indexing challenge with two hierarchical text categorization systems.  ...  We describe the adaptations required to deal with a large thesaurus like MeSH and a huge document collection and discuss the results obtained in the BioASQ challenge and the limitations of both approaches  ...  Acknowledgements Research reported in this paper has been partially funded by "Ministerio de Economía y Competitividad" and FEDER under the project TIN2010-18552-C03-01, by "Xunta de Galicia" under the  ... 
dblp:conf/clef/Ribadas-PenaIBR13 fatcat:lkzdp6jbyzhjvae4a3xj3mwchq

Sentiment Analysis Using Machine Learning Approach

Andreea-Maria Copaceanu
2021 Ovidius University Annals: Economic Sciences Series  
The paper proposes a comparison between four text classification algorithms - Naïve Bayes, Support Vector Machine, Decision Tree and Random Forest, using different feature extraction techniques, such as  ...  Our experiments revealed that Support Vector Machine achieves the best results and is very suitable for classification of the sentiment on product reviews.  ...  (Bansal, 2018) applied deep learning methods such as, CBOW and skip-gram, with different machine learning algorithms: SVM, Naïve Bayes, Logistic Regression and Random Forest.  ... 
doaj:0182bdc144724c71abdaf29f7c56f655 fatcat:qj73wydaavfgdike4v34ejsooi
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