This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypernyms . Six binary classification tasks of varying difficulty are defined , and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation . Then we represent the texture images by ' bag-of-keypoints ' vectors . By analogy text classification , we use Probabilistic Latent Semantic Indexing ( PLSI ) and Non-negative Matrix Factorization ( NMF ) to perform unsupervised classification . The proposed approach is evaluated using the UIUC database which contains significant viewpoint and scale changes . This paper addresses the text classification problem that training data may derive from a different time period from the test data . We present a method of temporal-based term selection for timeline adaptation . Hence insufficiency of the evidences for an unknown virus gives rise to classification issues . Therefore risk prediction of viruses from protein sequence and its structure is an effective alternative to text based classification . In this work , Machine learning techniques are used for risk type classification of Papillomaviruses which is having high potentials to cause cancer . Also a parallel text classifying based on cloud computing is designed and implemented , which classify the unknown type texts . Experimental results show that the speed of text classifying increases with the number of Map tasks increasing . There is a brief overview of the market for such solutions in the article . The parameters used to construct the neural network architecture used to solve the problem of text data classification are described in detail . The result of the work is a functioning part of the DLP system , which allows monitoring the traffic of a corporate network via a web-interface and controlling the transfer of confidential data over this network using a one-dimensional convolutional neural network 1D CNN . Classification is a vastly researched topic in the field of data mining and text data , representing a significant portion of the information , naturally has acquired significant research interest . However , text data classification presents its own problems of high and sparse dimensionality , as attributes span over huge set of words of natural language and multi-label property as each document may belong to more than one class simultaneously . Any solution proposed to classify such data without considering these facts can not render optimum results . With two layers of C4.5 decision tree classifier , the method can divide the text image into caption text image , document image and scene text image . The text image classification is a two-step process . In the first place , the sample image is converted into gray image for histogram feature extraction . Semi-supervised learning ( SSL ) is the most practical approach for classification among machine learning algorithms . It is similar to the humans way of learning and thus has great applications in text/image classification , bioinformatics , artificial intelligence , robotics etc . Labeled data is hard to obtain in real life experiments and may need human experts with experimental equipments to mark the labels , which can be slow and expensive . The genetic selection of keywords set , the text frequencies of which are considered as attributes in text classification analysis , has been analyzed . The genetic optimization was performed on a set of words , which is the fraction of the frequency dictionary with given frequency limits . In the component description , uses XML components of the visual means to describe various aspects of the information . Text classification of components of the test achieves good results . document : Character - level Convolutional Networks for Text Classification This article offers an empirical exploration on the use of character - level convolutional networks ( ConvNets ) for text classification . ea:2011 for language modeling . Early applications include text classification . Recently , these representations were successfully applied to a range of structured prediction tasks . document : Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Convolutional neural network ( CNN ) is a neural network that can make use of the internal structure of data such as the 2D structure of image data . Recently , the paradigm of transfer learning has been introduced to enable effective learning strategies when auxiliary data obey a different probability distribution . A co-clustering based classification algorithm has been previously proposed to tackle cross-domain text classification . In this work , we extend the idea underlying this approach by making the latent semantic relationship between the two domains explicit . Cross domain text classification aims at leveraging the knowledge in labeled source domains to predict the unlabeled data in another domain , where the distribution is different from the source domains . This paper proposes a new algorithm for cross domain classification in a multi-view learning perspective , named MulTi-View(MTV ) transfer classification . The distribution difference among multiple domains has been exploited for cross-domain text categorization in recent years . Along this line , we show two new observations in this study . In commonly proposed machine learning approaches , classifiers are automatically trained from pre-labeled documents : they can perform very accurate classification , but often require a consistent training set and notable computational effort . Methods for cross-domain text categorization have been proposed , allowing to leverage a set of labeled documents of one domain to classify those of another one . Most methods use advanced statistical techniques , usually involving tuning of parameters . With the help of domain knowledge relations , computed relevance between domain concepts , got domain classification feature weight . Finally domain text classification was realized . An experiment in the Yunnan tourism domain was carried on to confirm that domain knowledge relations have a good influence on the domain text classification . In this paper , a new text classification model is proposed , which is based on the domain knowledge relations . This model adopts the support vector machine study algorithm , combine statistic samples and domain terminology to make up classification feature space , and calculate the similarity between domain conceptions , so that classification characteristic is entrusted with certain weight , realizing domain text classification . The new model has been made use of to carry out a text classification experiment about YunNan travel domain and non-travel domain . The distribution difference among multiple data domains has been considered for the cross-domain text classification problem . In this study , we show two new observations along this line . We have built classifiers that can identify if a potential additional resource is about computing education . We approached this as a cross-domain text classification problem and developed suitable methods for feature extraction and bootstrapping for classifier training . Our experiments on three datasets of computing education metadata records show our approach can enhance the quality and quantity of records being added to Ensemble . Cross-domain learning text classification aims to train an accurate model for a target domain by using labeled text data from a source domain with different but related data distributions . To narrow the data distribution gap between different domains , most of the previous approaches utilize the bag-of-words model to obtain latent features representation of the text . In this chapter , we will review some most recent developments in transfer learning for text mining , explain related algorithms in detail , and project future developments of this field . We focus on two important topics : cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images . This paper describes our contribution to SemEval 2019 Task 5 : Hateval . We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features . For this purpose , we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them . Despite the potential business relevance of this research thread , the existing ad hoc solutions are still not scalable with real large text sets . Scalable Deep Learning techniques have been effectively applied to in-domain text classification , by training and categorising documents belonging to the same domain . This work analyses the cross-domain efficacy of a well-known unsupervised Deep Learning approach for text mining , called Paragraph Vector , comparing its performance with a method based on Markov Chain developed ad hoc for cross-domain sentiment classification . A major contribution of our work is that with BIG , a large amount of worldwide knowledge can be easily adapted and used for learning in the target domain . We conduct experiments on several real-world cross-domain text classification tasks and demonstrate that our proposed approach can outperform several existing domain adaptation approaches significantly . This paper introduces PyDCI , a new implementation of Distributional Correspondence Indexing ( DCI ) written in Python . DCI is a transfer learning method for cross - domain and cross - lingual text classification for which we had provided an implementation ( here called JaDCI ) built on top of JaTeCS , a Java framework for text classification . PyDCI is a stand - alone version of DCI that exploits scikit - learn and the SciPy stack . Various techniques as text analytics , word embedding , supervised and unsupervised learning were explored , extracting key concepts or entities . Attribution and relational similarity was explored using techniques as supervised text classification or unsupervised , by word vector representation and embedding using word2vdec . The aim was to explain overall satisfaction combining structured data ( collected with closed questions survey , using 7 points Likert scale ) enhanced with text classification of opinions , related to best and worst applications , along with explanations . The paper explores the semi-supervised text classification which is applied to different types of vectors that are generated from the text documents . KNN algorithm is employed in the process of semi-supervised text classification , and results obtained are encouraging . Text Categorization Using Bootstrapping . This paper proposes a semi supervised text categorization using bootstrapping . The System uses the Maximum Entropy Model as the text classifier . It utilizes priori labeled data to guide unsupervised text clustering process on the basis of the traditional text clustering method , and gets better clustering results . Semi-supervised text clustering has recently become a topic of significant interest . The complexity of document corpora has led to considerable interest in applying hierarchical statistical models based on what are called topic . Most existing studies focus on long texts and achieve unsatisfactory performance on short texts due to the sparsity and limited labeled data . In this paper , we propose a novel heterogeneous graph neural network based method for semi-supervised short text classification , leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph . In particular , we first present a flexible HIN ( heterogeneous information network ) framework for modeling the short texts , which can integrate any type of additional information as well as capture their relations to address the semantic sparsity . Those traditional classification methods can not be directly used in such tasks . This paper proposed a closeness-based method to solve this semi-supervised text categorization problem . It firstly extracts a reliable negative set from the unlabeled set , and then uses the closeness-based algorithm to enlarge initially extracted reliable negative set to a proper size . We address the problem of predicting which of several frequently used answers a user will choose to respond to an email . We map the problem to a semi-supervised text classification problem . In a case study with emails that have been sent to a corporate customer service department , we investigate the ability of the naive Bayesian and support vector classifier to identify the appropriate answers to emails . This paper investigates a framework that discovers pair-wise constraints for semi-supervised text document clustering . An active learning approach is proposed to select informative document pairs for obtaining user feedbacks . Non-parametric , most effective to put into effect ok-nearest neighbour algorithm is hired for free-go with the flow textual classification . The outcomes received conclude that semi-supervised textual class can be carried out without loss in category accuracy where restrained skilled records is to be had , as the accuracies of the gaining knowledge of model in supervised and emi-supervised coincide with each other . This thesis also proposes feature affinity and cluster regularization , which uses feature relationships learned from unlabeled data to regularize training . This regularization scheme converts supervised learning text classification to semi-supervised , and it is simple to control the relative importance of the knowledge learned from unlabeled data . Using this method , features that are unseen in the labeled data get non-zero weights due to their relationships to seen features . The discriminator of DCGAN can serve as a robust feature extractor . Salimans achieve a state - of - art result in semi - supervised classification and improves the visual quality of GANs . InfoGAN learns interpretable representations by introducing latent codes . section : Experiments We test our model in a number of experiments : semi - supervised document classification in citation networks , semi - supervised entity classification in a bipartite graph extracted from a knowledge graph , an evaluation of various graph propagation models and a run - time analysis on random graphs . subsection : Datasets subsection : Semi - Supervised Node Embeddings On this simple example of a GCN applied to the karate club network it is interesting to observe how embeddings react during training on a semi - supervised classification task . Such a visualization ( see Figure [ reference ] ) provides insights into how the GCN model can make use of the graph structure ( and of features extracted from the graph structure at later layers ) to learn embeddings that are useful for a classification task . ’s prediction . This method was applied successfully to semi - supervised text classification by miyato2016adversarial . ELMo . Supervised learning is difficult to apply to NLP problems because labels are expensive . Following , and , we train unsupervised text models on large amounts of unlabelled text data , and transfer the model features to small supervised text problems . The supervised text classification problem used for transfer is binary sentiment on the Stanford Sentiment Treebank ( SST ) .
This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypernyms . Six binary classification tasks of varying difficulty are defined , and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation .
Then we represent the texture images by ' bag-of-keypoints ' vectors . By analogy text classification , we use Probabilistic Latent Semantic Indexing ( PLSI ) and Non-negative Matrix Factorization ( NMF ) to perform unsupervised classification . The proposed approach is evaluated using the UIUC database which contains significant viewpoint and scale changes .
This paper addresses the text classification problem that training data may derive from a different time period from the test data . We present a method of temporal-based term selection for timeline adaptation .
Hence insufficiency of the evidences for an unknown virus gives rise to classification issues . Therefore risk prediction of viruses from protein sequence and its structure is an effective alternative to text based classification . In this work , Machine learning techniques are used for risk type classification of Papillomaviruses which is having high potentials to cause cancer .
Also a parallel text classifying based on cloud computing is designed and implemented , which classify the unknown type texts . Experimental results show that the speed of text classifying increases with the number of Map tasks increasing .
There is a brief overview of the market for such solutions in the article . The parameters used to construct the neural network architecture used to solve the problem of text data classification are described in detail . The result of the work is a functioning part of the DLP system , which allows monitoring the traffic of a corporate network via a web-interface and controlling the transfer of confidential data over this network using a one-dimensional convolutional neural network 1D CNN .
Classification is a vastly researched topic in the field of data mining and text data , representing a significant portion of the information , naturally has acquired significant research interest . However , text data classification presents its own problems of high and sparse dimensionality , as attributes span over huge set of words of natural language and multi-label property as each document may belong to more than one class simultaneously . Any solution proposed to classify such data without considering these facts can not render optimum results .
With two layers of C4.5 decision tree classifier , the method can divide the text image into caption text image , document image and scene text image . The text image classification is a two-step process . In the first place , the sample image is converted into gray image for histogram feature extraction .
Semi-supervised learning ( SSL ) is the most practical approach for classification among machine learning algorithms . It is similar to the humans way of learning and thus has great applications in text/image classification , bioinformatics , artificial intelligence , robotics etc . Labeled data is hard to obtain in real life experiments and may need human experts with experimental equipments to mark the labels , which can be slow and expensive .
The genetic selection of keywords set , the text frequencies of which are considered as attributes in text classification analysis , has been analyzed . The genetic optimization was performed on a set of words , which is the fraction of the frequency dictionary with given frequency limits .
In the component description , uses XML components of the visual means to describe various aspects of the information . Text classification of components of the test achieves good results .
document : Character - level Convolutional Networks for Text Classification This article offers an empirical exploration on the use of character - level convolutional networks ( ConvNets ) for text classification .
ea:2011 for language modeling . Early applications include text classification . Recently , these representations were successfully applied to a range of structured prediction tasks .
document : Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Convolutional neural network ( CNN ) is a neural network that can make use of the internal structure of data such as the 2D structure of image data .
Recently , the paradigm of transfer learning has been introduced to enable effective learning strategies when auxiliary data obey a different probability distribution . A co-clustering based classification algorithm has been previously proposed to tackle cross-domain text classification . In this work , we extend the idea underlying this approach by making the latent semantic relationship between the two domains explicit .
Cross domain text classification aims at leveraging the knowledge in labeled source domains to predict the unlabeled data in another domain , where the distribution is different from the source domains . This paper proposes a new algorithm for cross domain classification in a multi-view learning perspective , named MulTi-View(MTV ) transfer classification .
The distribution difference among multiple domains has been exploited for cross-domain text categorization in recent years . Along this line , we show two new observations in this study .
In commonly proposed machine learning approaches , classifiers are automatically trained from pre-labeled documents : they can perform very accurate classification , but often require a consistent training set and notable computational effort . Methods for cross-domain text categorization have been proposed , allowing to leverage a set of labeled documents of one domain to classify those of another one . Most methods use advanced statistical techniques , usually involving tuning of parameters .
With the help of domain knowledge relations , computed relevance between domain concepts , got domain classification feature weight . Finally domain text classification was realized . An experiment in the Yunnan tourism domain was carried on to confirm that domain knowledge relations have a good influence on the domain text classification .
In this paper , a new text classification model is proposed , which is based on the domain knowledge relations . This model adopts the support vector machine study algorithm , combine statistic samples and domain terminology to make up classification feature space , and calculate the similarity between domain conceptions , so that classification characteristic is entrusted with certain weight , realizing domain text classification . The new model has been made use of to carry out a text classification experiment about YunNan travel domain and non-travel domain .
The distribution difference among multiple data domains has been considered for the cross-domain text classification problem . In this study , we show two new observations along this line .
We have built classifiers that can identify if a potential additional resource is about computing education . We approached this as a cross-domain text classification problem and developed suitable methods for feature extraction and bootstrapping for classifier training . Our experiments on three datasets of computing education metadata records show our approach can enhance the quality and quantity of records being added to Ensemble .
Cross-domain learning text classification aims to train an accurate model for a target domain by using labeled text data from a source domain with different but related data distributions . To narrow the data distribution gap between different domains , most of the previous approaches utilize the bag-of-words model to obtain latent features representation of the text .
In this chapter , we will review some most recent developments in transfer learning for text mining , explain related algorithms in detail , and project future developments of this field . We focus on two important topics : cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images .
This paper describes our contribution to SemEval 2019 Task 5 : Hateval . We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features . For this purpose , we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them .
Despite the potential business relevance of this research thread , the existing ad hoc solutions are still not scalable with real large text sets . Scalable Deep Learning techniques have been effectively applied to in-domain text classification , by training and categorising documents belonging to the same domain . This work analyses the cross-domain efficacy of a well-known unsupervised Deep Learning approach for text mining , called Paragraph Vector , comparing its performance with a method based on Markov Chain developed ad hoc for cross-domain sentiment classification .
A major contribution of our work is that with BIG , a large amount of worldwide knowledge can be easily adapted and used for learning in the target domain . We conduct experiments on several real-world cross-domain text classification tasks and demonstrate that our proposed approach can outperform several existing domain adaptation approaches significantly .
This paper introduces PyDCI , a new implementation of Distributional Correspondence Indexing ( DCI ) written in Python . DCI is a transfer learning method for cross - domain and cross - lingual text classification for which we had provided an implementation ( here called JaDCI ) built on top of JaTeCS , a Java framework for text classification . PyDCI is a stand - alone version of DCI that exploits scikit - learn and the SciPy stack .
Various techniques as text analytics , word embedding , supervised and unsupervised learning were explored , extracting key concepts or entities . Attribution and relational similarity was explored using techniques as supervised text classification or unsupervised , by word vector representation and embedding using word2vdec . The aim was to explain overall satisfaction combining structured data ( collected with closed questions survey , using 7 points Likert scale ) enhanced with text classification of opinions , related to best and worst applications , along with explanations .
The paper explores the semi-supervised text classification which is applied to different types of vectors that are generated from the text documents . KNN algorithm is employed in the process of semi-supervised text classification , and results obtained are encouraging .
Text Categorization Using Bootstrapping . This paper proposes a semi supervised text categorization using bootstrapping . The System uses the Maximum Entropy Model as the text classifier .
It utilizes priori labeled data to guide unsupervised text clustering process on the basis of the traditional text clustering method , and gets better clustering results . Semi-supervised text clustering has recently become a topic of significant interest . The complexity of document corpora has led to considerable interest in applying hierarchical statistical models based on what are called topic .
Most existing studies focus on long texts and achieve unsatisfactory performance on short texts due to the sparsity and limited labeled data . In this paper , we propose a novel heterogeneous graph neural network based method for semi-supervised short text classification , leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph . In particular , we first present a flexible HIN ( heterogeneous information network ) framework for modeling the short texts , which can integrate any type of additional information as well as capture their relations to address the semantic sparsity .
Those traditional classification methods can not be directly used in such tasks . This paper proposed a closeness-based method to solve this semi-supervised text categorization problem . It firstly extracts a reliable negative set from the unlabeled set , and then uses the closeness-based algorithm to enlarge initially extracted reliable negative set to a proper size .
We address the problem of predicting which of several frequently used answers a user will choose to respond to an email . We map the problem to a semi-supervised text classification problem . In a case study with emails that have been sent to a corporate customer service department , we investigate the ability of the naive Bayesian and support vector classifier to identify the appropriate answers to emails .
This paper investigates a framework that discovers pair-wise constraints for semi-supervised text document clustering . An active learning approach is proposed to select informative document pairs for obtaining user feedbacks .
Non-parametric , most effective to put into effect ok-nearest neighbour algorithm is hired for free-go with the flow textual classification . The outcomes received conclude that semi-supervised textual class can be carried out without loss in category accuracy where restrained skilled records is to be had , as the accuracies of the gaining knowledge of model in supervised and emi-supervised coincide with each other .
This thesis also proposes feature affinity and cluster regularization , which uses feature relationships learned from unlabeled data to regularize training . This regularization scheme converts supervised learning text classification to semi-supervised , and it is simple to control the relative importance of the knowledge learned from unlabeled data . Using this method , features that are unseen in the labeled data get non-zero weights due to their relationships to seen features .
The discriminator of DCGAN can serve as a robust feature extractor . Salimans achieve a state - of - art result in semi - supervised classification and improves the visual quality of GANs . InfoGAN learns interpretable representations by introducing latent codes .
section : Experiments We test our model in a number of experiments : semi - supervised document classification in citation networks , semi - supervised entity classification in a bipartite graph extracted from a knowledge graph , an evaluation of various graph propagation models and a run - time analysis on random graphs . subsection : Datasets
subsection : Semi - Supervised Node Embeddings On this simple example of a GCN applied to the karate club network it is interesting to observe how embeddings react during training on a semi - supervised classification task . Such a visualization ( see Figure [ reference ] ) provides insights into how the GCN model can make use of the graph structure ( and of features extracted from the graph structure at later layers ) to learn embeddings that are useful for a classification task .
’s prediction . This method was applied successfully to semi - supervised text classification by miyato2016adversarial . ELMo .
Supervised learning is difficult to apply to NLP problems because labels are expensive . Following , and , we train unsupervised text models on large amounts of unlabelled text data , and transfer the model features to small supervised text problems . The supervised text classification problem used for transfer is binary sentiment on the Stanford Sentiment Treebank ( SST ) .
Text Classification Using WordNet Hypernyms Unsupervised texture classification: Automatically discover and classify texture patterns The Effect of Temporal-based Term Selection for Text Classification Prediction of HPV Risk Types from Protein Secondary Structure Algorithm of Text Categorization Based on Cloud Computing ПРОГРАММНАЯ РЕАЛИЗАЦИЯ МОДУЛЯ DLP-СИСТЕМЫ ДЛЯ МОНИТОРИНГА И АНАЛИЗА ТРАФИКА КОРПОРАТИВНОЙ СЕТИ С ИСПОЛЬЗОВАНИЕМ МАШИННОГО ОБУЧЕНИЯ Predicted probability enhancement for multi-label text classification using class label pair association A text image classification research based on the underlying image feature combination Semi-supervised and self-evolving learning algorithms with application to anomaly detection in cloud computing Genetic Optimization of Keywords Subset in the Classification Analysis of Texts Authorship Design and Implementation of a Text Classification Component Character-level Convolutional Networks for Text Classification Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Using Wikipedia for Co-clustering Based Cross-Domain Text Classification Multi-view Cross Domain Sentiment Classification Based on Canonical Correlation Analysis Erratum to "Mining Distinction and Commonality across Multiple Domains Using Generative Model for Text Classification" Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains Study on the construction of domain text classification model with the help of domain knowledge The effects of domain knowledge relations on domain text classification Collaborative Dual-PLSA: mining distinction and commonality across multiple domains for text classification Using ACM DL paper metadata as an auxiliary source for building educational collections Knowledge-based document embedding for cross-domain text classification Transfer Learning for Text Mining Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification On Deep Learning in Cross-Domain Sentiment Classification Bridging Domains Using World Wide Knowledge for Transfer Learning Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments The Value of Structured and Unstructured Content Analytics of Employees’ Opinion Mining Different similarity measures in semi-supervised text classification Semi-Supervised Text Categorization Using Bootstrapping A Semi-Supervised Text Clustering Algorithm with Word Distribution Weights Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification A Closeness-Based Semi-Supervised Text Classification Method Email answering assistance by semi-supervised text classification Semi-supervised Document Clustering via Active Learning with Pairwise Constraints Semi-Supervised Distributional Vector Generation Techniques for Text Classification Learning features for text classification Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Semi-Supervised Classification with Graph Convolutional Networks Semi-Supervised Sequence Modeling with Cross-View Training Practical Text Classification With Large Pre-Trained Language Models
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WordNet@ACL/COLING ALTA EAIS NIPS ACL HLT-NAACL ICDM CIKM JCDL IJCNN Mining Text Data SemEval@NAACL-HLT KDIR ArXiv EMNLP/IJCNLP (1) ICDM 2017 IEEE International Conference on Computer Vision (ICCV) ICLR EMNLP ArXiv
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