Both these results were statistically significant compared to the baseline " raw text " representation . We discuss in detail the effects of roles on each NE and on semantic categories of noun and verb features in terms of accuracy , precision/recall and F-score measures for the text classification task . In this paper , we address the text classification problem that a period of time created test data is different from the training data , and present a method for text classification based on temporal adaptation . We first applied lexical chains for the training data to collect terms with semantic relatedness , and created sets ( we call these Sem sets ) . In this paper , supervised learning of social networks from a set of documents is proposed . Given a small subset of relations between the individuals , the problem of learning social network is translated into a text classification problem . Relation between each pair of individuals is represented by a vector of words produced from merging all documents associated with these two individuals . Thus , the addresses can be exploited to provide additional information about the technological area . We present a geo-enriched classifier joining established methods for text-based classification with location-based topic prediction . Since the location-based prediction is not applicable to all cases , we provide a method to regulate the impact of the spatial predictor for these cases . In this paper , we discuss an application of SVM ensemble classifiers to the problem , and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes . Using a matrix of closeness and a set of additional fusion rules , the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results . An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach . At the same time , classification precision is advanced using the way of selecting balance candidate samples after estimating the loss of classifying error . The model of text classifying using non-labeled training sample with more extensive application is realized . In high dimensional data , groups of objects often exist in subspaces rather than in the entire space . For example , in text data classification , groups of documents of different types are categorized by different subsets of terms . The terms for one group may not occur in the samples of other groups . Results indicate that SVM shows the best performance during the training and prediction stages . This study contributes to the field of network troubleshooting , and the field of text data classification . The semantic information provided by an image can be used in different applications such as content based image retrieval , sign board identification etc . Text information extraction comprises of text image classification , text detection , localization , segmentation , enhancement and recognition . This paper contains a quick review on various text localization methods for localizing texts from natural scene images . KeywoRDS Confidence Computation , Document Image , Fuzzy Matching , Handwritten Documents , Performance Analysis , Printed Documents , SVM , Text Image Classification , Word Association Abstract The genetic selection of keyword sets , the text frequencies of which are considered as attributes in text classification analysis , has been analysed . The genetic optimization was performed on a set of words , which is the fraction of the frequency dictionary with given frequency limits . ” . In Text Classification of Methods , there is a sentence “ Where P(B|A ) is the probability of B appearance when A is known ? The value P(A|B ) is the probability of an appearance if B is known . II . TEXT CLASSIFICATION Text classification is a commonly used technique as a basis for applications in document processing and Visualization , web mining , surveillance technology , patent Analysis , etc . Evaluation of different methods of Experiments , the basis for the choice of a classifier , is a Solution for a specific problem . Proposes a new evaluation of text classification vector , and uses it to discuss two models : the optimization model and the collection of the optimization model . The optimization model is used to distinguish the main classes and related classes , and clearly point out that class c ' of the document d_i is always better than class c".The collection optimization model is the premise of the election of a main class to identify the co-occurrence patterns between the various related classes . both RNN variants like the multiplicative LSTM ( mLSTM ) , as well as the attention - based Transformer network – can be trained efficiently over very large datasets , and that the resulting models can be transferred to downstream language understanding problems , often matching or exceeding the previous state of the art approaches on academic datasets . However , how well do these models perform on practical text classification problems , with real world data ? In this work , we train both mLSTM and Transformer language models on a large 40 GB text dataset , then transfer those models to two text classification problems : binary sentiment ( including Neutral labels ) , and multidimensional emotion classification based on the Plutchik wheel of emotions . We suggest that our approach creates models with good generalization to increasingly difficult text classification problems , and we offer ablation studies to demonstrate that effect . It is difficult to fit a single model for text classification across domains , due to unknown words , specialized context , colloquial language , and other differences between domains . For example , words such as war and sick are not necessarily negative in the context of video games , which are significantly represented in our dataset . section : Conclusion In this work we demonstrate that unsupervised pretraining and finetuning provides a flexible framework that is effective for difficult text classification tasks . We noticed that the finetuning was especially effective with the Transformer network , when transferring to downstream tasks with noisy labels and specialized context . 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 . This paper studies CNN on text categorization to exploit the 1D structure ( namely , word order ) of text data for accurate prediction . Instead of using low - dimensional word vectors as input as is often done , we directly apply CNN to high - dimensional text data , which leads to directly learning embedding of small text regions for use in classification . Deep learning based neural network models have achieved great success in many NLP tasks , including learning distributed word , sentence and document representation , parsing , statistical machine translation , sentiment classification , etc . Learning distributed sentence representation through neural network models requires little external domain knowledge and can reach satisfactory results in related tasks like sentiment classification , text categorization . In many recent sentence representation learning works , neural network models are constructed upon either the input word sequences or the transformed syntactic parse tree . While these models achieve very good performance in practice , they tend to be relatively slow both at train and test time , limiting their use on very large datasets . Meanwhile , linear classifiers are often considered as strong baselines for text classification problems . Despite their simplicity , they often obtain state - of - the - art performances if the right features are used . Empirical results show that Paragraph Vectors outperform bag - of - words models as well as other techniques for text representations . Finally , we achieve new state - of - the - art results on several text classification and sentiment analysis tasks . DistributedRepresentationsofSentencesandDocuments The VDCNN model uses a - max pooling layer followed by three fully connected ( FC ) layers to perform the classification task ( Fig . 4a ) . Although this approach is the traditional architecture choice for text classification CNNs , it introduces a significant number of parameter in the network . The resulting number of the FC layers parameters ( ) aforementioned is presented below , for a problem with four target classes : [ ] [ ] Instead of maintaining these fully connected layers , we directly aggregate the output of the last convolutional block through the usage of an average pooling layer . [ reference ] . The RPN is used for proposing text regions and the Fast R - CNN model [ reference ] is modified to do text region classification , refinement and inclined box prediction . - The arbitrary - oriented text detection problem is formulated as a multi - task problem . In particular , the Maximally Stable Extremal Regions ( MSER ) based methods used to achieve good performances in ICDAR 2015 [ reference ] and ICDAR 2013 [ reference ] competitions . These traditional methods adopt a bottom - up strategy and often needs several steps to detect texts ( e.g. , character detection , text line construction and text line classification ) . The general object detection is a hot research area recently . The experiments performed confirm that the automatically obtained fuzzy ontologies are good representation of the user ’s preferences . In order to test the applicability of the obtained ontologies , a text categorization experiment has been proposed and the obtained results indicate that the approach can be applied with satisfactory results and warrants further research . A Study in Text Categorization ) . This paper describes a study on text categorization using a character n­grams approach for the morphological normalization . In recent work , this approach has emerged as a way to simplify the normalization of terms . Authorship attribution may be considered as a text categorization problem . Text categorization requires a large number of training examples which are particularly difficult to obtain in the case of authorship attribution task . We address the e-rulemaking problem of categorizing public comments according to the issues that they address . In contrast to previous text categorization research in e-rulemaking [ 5 , 6 ] , and in an attempt to more closely duplicate the comment analysis process in federal agencies , we employ a set of rule-specific categories , each of which corresponds to a significant issue raised in the comments . We describe the creation of a corpus to support this text categorization task and report interannotator agreement results for a group of six annotators . In this environment , it is more necessary than ever before that people extract comprehensive , efficient , and valuable information from a wide range of cultural information . Therefore , text categorization research has become more important . The paper improved the precision of the traditional text categorization by the process that we mended the weight of words and mined potential keywords , then found their relationship . By comparing , analyzing and studying them , a better method of feature weighting ascertainment is presented here . This method can improve the accuracy of text categorizing efficiently , which is proved by experiments . The articles in the corpus are of different style , length and topics . Making authoritative corpus and standard of text categorizing are suggested for evaluation and application of text categorizing model . In direct proportion to the heavy increase of online information data , the attention to text categorization ( classification ) has also increased . In text categorization problem , namely , text classification , the goal is to classify the documents into predefined classes ( categories or labels ) . The second one is based on a dynamic structure of sequences of documents , referred to as cases , identified within each category . While the former problem may be addressed using one of the well known techniques of text categorization ( classification ) , the latter seems to require some distinct approaches due to the fact that the set of cases is unknown in advance , as well as due to the assumed limited number of training documents , if a case should be interpreted as a classic category . In the paper , we discuss the problem in a more detail as well as show the applicability of an intuitively appealing two stage approach to solving the problem of such a multiaspect text categorization . The use of word embedding models and deep learning algorithms are currently the most common and popular trends to enhance the overall performance of a text classification/categorization system . Word embedding models are vectors that provide a mapping of words with similar meaning to own a similar representation which is learned from a corpus . Second , we adopt the Generalized Vector Space model ( GVSM ) in which the term-term association is well established . For text classification/categorization , we investigate a steepest descent induction algorithm combined with a two-level preference relation on user ranking . The performance of the proposed algorithm is evaluated experimentally . Previously the organization of information was generally done manually , by matching the document contents to some pre-defined categories . There are two approaches for this text-based categorization : manual and automatic . In the manual approach , a human expert performs the classification task , and in the second case supervised classifiers are used to automatically classify resources . It provides conceptual views of the collected documents and has important applications in the real world . Text based categorization is made use of for document classification with pattern recognition and machine learning . Advantages of a number of classification algorithms have been studied in this paper to classify documents . Three different semantic categories were considered and five texts selected from each category for submission to a text categorization procedure using the proposed method . The results and the statistical evaluation of this procedure showed that the proposed method may be characterized as highly accurate for text categorization purposes . Main aim of our research activities is to design and develop experimental cloud platform for text mining tasks . In this particular paper we describe the design and implementation of a distributed tree-based algorithm for text categorization purposes . We used our own implementation of decision tree classification algorithm and used Gridgain framework for its cloud implementation . Our aim is to develop a prototype fusion capability that can categorize , fuse and present intelligence information to military officers . To date , our focus has been on text information categorization using both existing text classification techniques ( e.g. Weighted Feature Vector ( WFV ) classification ) and machine learning algorithms based on inductive logic programming ( ILP ) and natural language processing techniques . The algorithms have been used to automatically assign documents to a pre-existing set of categories by correlating text within the documents to text relating to classifications/categories . Abstract In multi-label text classification , considering the correlation between labels is an important yet challenging task due to the combination possibility in the label space increasing exponentially . In recent years , neural network models have been widely applied and gradually achieved satisfactory performance in this field . Firstly , Chinese fault texts are sequentially preprocessed by word segmentation , stop words removal and feature representation . Then , we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm . The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification . In this paper , we propose a framework that does not depend on tagging history of the resource or user and thereby capable of suggesting tags to the resources which are being submitted to the system first time . We model tag recommendation task as multi-label text classification problem and use Naive Bayes classifier as the base learner of the multilabel classifier . We experiment with Boolean , bag-of-words and term frequency-inverse document frequency ( TFIDF ) representation of the resources and fit appropriate distribution to the data based on the representation used . We examine supervised learning for multi-class , multi-label text classification . We are interested in exploring classification in a real-world setting , where the distribution of labels may change dynamically over time . We explore supervised learning for multi-class , multi-label text classification , focusing on real-world settings , where the distribution of labels changes dynamically over time . We use the PULS Information Extraction system to collect information about the distribution of class labels over named entities found in text . However , the accuracy of centroid-based classifiers is inferior to SVM , mainly because centroids found during construction are far from perfect locations . We design a fast Class-Feature-Centroid ( CFC ) classifier for multi-class , single-label text categorization . In CFC , a centroid is built from two important class distributions : inter-class term index and inner-class term index . This thesis studies Parametric Mixture Models ( PMMs ) . They are efficient statistical models to solve multi-label text categorization problem . Conventional machine learning models usually training binary classifiers for predicting multi-label problem . In a joint effort involving federal , state and city business regulating offices , the unification and automation of this codification process is in progress . As the input information is in the form of free textual entries , techniques used in multi-class , multi-label text categorization are well suited . Due to the fact that the possible codes are in the order of one thousand , this problem is under investigation through an approach based on a hierarchical use of Support Vector Machines ( SVM ) . A number of language-independent text pre-processing techniques , to support multi-class single-label text classification , are described and compared . A simple but effective statistical keyword identification approach is proposed , coupled with a number of phrase identification mechanisms . For large-scale data such as corpus text , intelligent learning methods have been effectively approached . In this paper , an artificial neural network with a quasi-Newton updating procedure is presented for multi-label multi-class text classification . This numerical unconstrained training technique , the Multi-Label extension of Log-Loss function using in Limited-memory Broyden – Fletcher – Goldfarb – Shanno algorithm ( ML4BFGS ) , provides a noteworthy opportunity for text mining and leads to a significant improvement in text classification performances . We have further utilized the correlation within the training data to propose a post processing step to refine the probability assigned to each of the test instances by SISC . Empirical evaluation on real world benchmark multi-class and multi-label text data sets and comparison with other state-of-the-art text classification as well as subspace clustering algorithms show that SISC provides superior performance for multi-label text data classification in a multi-class setting . We skip grid search since it is not feasible over 40 tasks . Although uniform scaling is the norm in the multi - label classification literature , single - task performance is significantly better . Our method outperforms baselines for significant majority of tasks and achieves comparable performance in rest . The KKT condition for this Lagrangian yields the desired result as section : B Additional Results on Multi - label Classification In this section , we present the experimental results we did not include in the main text . We thoroughly evaluate it on a number of multilabel classification tasks , and analyze its sensitivity across several parameters . section : Multi - Label Classification To facilitate the comparison between our method and the relevant baselines , we use the exact same datasets and experimental procedure as in [ reference ][ reference ] . Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods , and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations . We demonstrate the efficacy of node2vec over existing state - of - the - art techniques on multi - label classification and link prediction in several real - world networks from diverse domains . Taken together , our work represents a new way for efficiently learning state - of - the - art task - independent representations in complex networks . In particular , we design a novel framework , EXplicit interAction Model ( dubbed as EXAM ) , equipped with the interaction mechanism . We justified the proposed approach on several benchmark datasets including both multi - label and multi - class text classification tasks . Extensive experimental results demonstrate the superiority of the proposed method . In this work , we present a novel framework named EXAM which employs the interaction mechanism to explicitly compute the word - level interaction signals for the text classification . We apply the proposed EXAM on multi - class and multi - label text classifications . Experiments over several benchmark datasets verify the effectiveness of our proposed mechanism . RCV1 has 103 topic categories in a hierarchy , and one document may be associated with more than one topic . Performance on this task ( multi - label categorization ) is known to be sensitive to thresholding strategies , which are algorithms additional to the models we would like to test . Therefore , we also experimented with single - label categorization to assign one of 55 second - level topics to each document to directly evaluate models . The model includes an image analysis part and a caption generation part . In the image analysis part , we first use supervised learning to predict a set of attributes , based on words commonly found in image captions We solve this as a multi - label classification problem and train a corresponding deep CNN by minimizing an element - wise logistic loss function . Secondly , a fixed length vector is created for each image , whose length is the size of the attribute set . 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 ) . Some of these binary text examples are subtle . This signature ensures the implementation of numerous applications for managing effectively a corpus or collections of books ( e.g. information retrieval in digital libraries according to several criteria , or page categorization ) . To illustrate the effectiveness of the proposed page signature , a detailed experimental evaluation has been conducted in this article for assessing two possible categorization applications , unsupervised page classification and page stream segmentation . In the case of multi-page documents , the preservation of document contexts is a major requirement . To facilitate workflows involving very large amounts of paper scans , page stream segmentation ( PSS ) is the task to automatically separate a stream of scanned images into coherent multi-page documents . In a digitization project together with a German federal archive , we developed a novel approach for PSS based on convolutional neural networks ( CNN ) . In this manuscript we propose a novel method for jointly page stream segmentation and multi-page document classification . The end goal is to classify a stream of pages as belonging to different classes of documents .
Both these results were statistically significant compared to the baseline " raw text " representation . We discuss in detail the effects of roles on each NE and on semantic categories of noun and verb features in terms of accuracy , precision/recall and F-score measures for the text classification task .
In this paper , we address the text classification problem that a period of time created test data is different from the training data , and present a method for text classification based on temporal adaptation . We first applied lexical chains for the training data to collect terms with semantic relatedness , and created sets ( we call these Sem sets ) .
In this paper , supervised learning of social networks from a set of documents is proposed . Given a small subset of relations between the individuals , the problem of learning social network is translated into a text classification problem . Relation between each pair of individuals is represented by a vector of words produced from merging all documents associated with these two individuals .
Thus , the addresses can be exploited to provide additional information about the technological area . We present a geo-enriched classifier joining established methods for text-based classification with location-based topic prediction . Since the location-based prediction is not applicable to all cases , we provide a method to regulate the impact of the spatial predictor for these cases .
In this paper , we discuss an application of SVM ensemble classifiers to the problem , and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes . Using a matrix of closeness and a set of additional fusion rules , the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results . An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach .
At the same time , classification precision is advanced using the way of selecting balance candidate samples after estimating the loss of classifying error . The model of text classifying using non-labeled training sample with more extensive application is realized .
In high dimensional data , groups of objects often exist in subspaces rather than in the entire space . For example , in text data classification , groups of documents of different types are categorized by different subsets of terms . The terms for one group may not occur in the samples of other groups .
Results indicate that SVM shows the best performance during the training and prediction stages . This study contributes to the field of network troubleshooting , and the field of text data classification .
The semantic information provided by an image can be used in different applications such as content based image retrieval , sign board identification etc . Text information extraction comprises of text image classification , text detection , localization , segmentation , enhancement and recognition . This paper contains a quick review on various text localization methods for localizing texts from natural scene images .
KeywoRDS Confidence Computation , Document Image , Fuzzy Matching , Handwritten Documents , Performance Analysis , Printed Documents , SVM , Text Image Classification , Word Association
Abstract The genetic selection of keyword sets , the text frequencies of which are considered as attributes in text classification analysis , has been analysed . The genetic optimization was performed on a set of words , which is the fraction of the frequency dictionary with given frequency limits .
” . In Text Classification of Methods , there is a sentence “ Where P(B|A ) is the probability of B appearance when A is known ? The value P(A|B ) is the probability of an appearance if B is known .
II . TEXT CLASSIFICATION Text classification is a commonly used technique as a basis for applications in document processing and Visualization , web mining , surveillance technology , patent Analysis , etc . Evaluation of different methods of Experiments , the basis for the choice of a classifier , is a Solution for a specific problem .
Proposes a new evaluation of text classification vector , and uses it to discuss two models : the optimization model and the collection of the optimization model . The optimization model is used to distinguish the main classes and related classes , and clearly point out that class c ' of the document d_i is always better than class c".The collection optimization model is the premise of the election of a main class to identify the co-occurrence patterns between the various related classes .
both RNN variants like the multiplicative LSTM ( mLSTM ) , as well as the attention - based Transformer network – can be trained efficiently over very large datasets , and that the resulting models can be transferred to downstream language understanding problems , often matching or exceeding the previous state of the art approaches on academic datasets . However , how well do these models perform on practical text classification problems , with real world data ? In this work , we train both mLSTM and Transformer language models on a large 40 GB text dataset , then transfer those models to two text classification problems : binary sentiment ( including Neutral labels ) , and multidimensional emotion classification based on the Plutchik wheel of emotions .
We suggest that our approach creates models with good generalization to increasingly difficult text classification problems , and we offer ablation studies to demonstrate that effect . It is difficult to fit a single model for text classification across domains , due to unknown words , specialized context , colloquial language , and other differences between domains . For example , words such as war and sick are not necessarily negative in the context of video games , which are significantly represented in our dataset .
section : Conclusion In this work we demonstrate that unsupervised pretraining and finetuning provides a flexible framework that is effective for difficult text classification tasks . We noticed that the finetuning was especially effective with the Transformer network , when transferring to downstream tasks with noisy labels and specialized context .
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 . This paper studies CNN on text categorization to exploit the 1D structure ( namely , word order ) of text data for accurate prediction . Instead of using low - dimensional word vectors as input as is often done , we directly apply CNN to high - dimensional text data , which leads to directly learning embedding of small text regions for use in classification .
Deep learning based neural network models have achieved great success in many NLP tasks , including learning distributed word , sentence and document representation , parsing , statistical machine translation , sentiment classification , etc . Learning distributed sentence representation through neural network models requires little external domain knowledge and can reach satisfactory results in related tasks like sentiment classification , text categorization . In many recent sentence representation learning works , neural network models are constructed upon either the input word sequences or the transformed syntactic parse tree .
While these models achieve very good performance in practice , they tend to be relatively slow both at train and test time , limiting their use on very large datasets . Meanwhile , linear classifiers are often considered as strong baselines for text classification problems . Despite their simplicity , they often obtain state - of - the - art performances if the right features are used .
Empirical results show that Paragraph Vectors outperform bag - of - words models as well as other techniques for text representations . Finally , we achieve new state - of - the - art results on several text classification and sentiment analysis tasks . DistributedRepresentationsofSentencesandDocuments
The VDCNN model uses a - max pooling layer followed by three fully connected ( FC ) layers to perform the classification task ( Fig . 4a ) . Although this approach is the traditional architecture choice for text classification CNNs , it introduces a significant number of parameter in the network . The resulting number of the FC layers parameters ( ) aforementioned is presented below , for a problem with four target classes : [ ] [ ] Instead of maintaining these fully connected layers , we directly aggregate the output of the last convolutional block through the usage of an average pooling layer .
[ reference ] . The RPN is used for proposing text regions and the Fast R - CNN model [ reference ] is modified to do text region classification , refinement and inclined box prediction . - The arbitrary - oriented text detection problem is formulated as a multi - task problem .
In particular , the Maximally Stable Extremal Regions ( MSER ) based methods used to achieve good performances in ICDAR 2015 [ reference ] and ICDAR 2013 [ reference ] competitions . These traditional methods adopt a bottom - up strategy and often needs several steps to detect texts ( e.g. , character detection , text line construction and text line classification ) . The general object detection is a hot research area recently .
The experiments performed confirm that the automatically obtained fuzzy ontologies are good representation of the user ’s preferences . In order to test the applicability of the obtained ontologies , a text categorization experiment has been proposed and the obtained results indicate that the approach can be applied with satisfactory results and warrants further research .
A Study in Text Categorization ) . This paper describes a study on text categorization using a character n­grams approach for the morphological normalization . In recent work , this approach has emerged as a way to simplify the normalization of terms .
Authorship attribution may be considered as a text categorization problem . Text categorization requires a large number of training examples which are particularly difficult to obtain in the case of authorship attribution task .
We address the e-rulemaking problem of categorizing public comments according to the issues that they address . In contrast to previous text categorization research in e-rulemaking [ 5 , 6 ] , and in an attempt to more closely duplicate the comment analysis process in federal agencies , we employ a set of rule-specific categories , each of which corresponds to a significant issue raised in the comments . We describe the creation of a corpus to support this text categorization task and report interannotator agreement results for a group of six annotators .
In this environment , it is more necessary than ever before that people extract comprehensive , efficient , and valuable information from a wide range of cultural information . Therefore , text categorization research has become more important . The paper improved the precision of the traditional text categorization by the process that we mended the weight of words and mined potential keywords , then found their relationship .
By comparing , analyzing and studying them , a better method of feature weighting ascertainment is presented here . This method can improve the accuracy of text categorizing efficiently , which is proved by experiments .
The articles in the corpus are of different style , length and topics . Making authoritative corpus and standard of text categorizing are suggested for evaluation and application of text categorizing model .
In direct proportion to the heavy increase of online information data , the attention to text categorization ( classification ) has also increased . In text categorization problem , namely , text classification , the goal is to classify the documents into predefined classes ( categories or labels ) .
The second one is based on a dynamic structure of sequences of documents , referred to as cases , identified within each category . While the former problem may be addressed using one of the well known techniques of text categorization ( classification ) , the latter seems to require some distinct approaches due to the fact that the set of cases is unknown in advance , as well as due to the assumed limited number of training documents , if a case should be interpreted as a classic category . In the paper , we discuss the problem in a more detail as well as show the applicability of an intuitively appealing two stage approach to solving the problem of such a multiaspect text categorization .
The use of word embedding models and deep learning algorithms are currently the most common and popular trends to enhance the overall performance of a text classification/categorization system . Word embedding models are vectors that provide a mapping of words with similar meaning to own a similar representation which is learned from a corpus .
Second , we adopt the Generalized Vector Space model ( GVSM ) in which the term-term association is well established . For text classification/categorization , we investigate a steepest descent induction algorithm combined with a two-level preference relation on user ranking . The performance of the proposed algorithm is evaluated experimentally .
Previously the organization of information was generally done manually , by matching the document contents to some pre-defined categories . There are two approaches for this text-based categorization : manual and automatic . In the manual approach , a human expert performs the classification task , and in the second case supervised classifiers are used to automatically classify resources .
It provides conceptual views of the collected documents and has important applications in the real world . Text based categorization is made use of for document classification with pattern recognition and machine learning . Advantages of a number of classification algorithms have been studied in this paper to classify documents .
Three different semantic categories were considered and five texts selected from each category for submission to a text categorization procedure using the proposed method . The results and the statistical evaluation of this procedure showed that the proposed method may be characterized as highly accurate for text categorization purposes .
Main aim of our research activities is to design and develop experimental cloud platform for text mining tasks . In this particular paper we describe the design and implementation of a distributed tree-based algorithm for text categorization purposes . We used our own implementation of decision tree classification algorithm and used Gridgain framework for its cloud implementation .
Our aim is to develop a prototype fusion capability that can categorize , fuse and present intelligence information to military officers . To date , our focus has been on text information categorization using both existing text classification techniques ( e.g. Weighted Feature Vector ( WFV ) classification ) and machine learning algorithms based on inductive logic programming ( ILP ) and natural language processing techniques . The algorithms have been used to automatically assign documents to a pre-existing set of categories by correlating text within the documents to text relating to classifications/categories .
Abstract In multi-label text classification , considering the correlation between labels is an important yet challenging task due to the combination possibility in the label space increasing exponentially . In recent years , neural network models have been widely applied and gradually achieved satisfactory performance in this field .
Firstly , Chinese fault texts are sequentially preprocessed by word segmentation , stop words removal and feature representation . Then , we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm . The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification .
In this paper , we propose a framework that does not depend on tagging history of the resource or user and thereby capable of suggesting tags to the resources which are being submitted to the system first time . We model tag recommendation task as multi-label text classification problem and use Naive Bayes classifier as the base learner of the multilabel classifier . We experiment with Boolean , bag-of-words and term frequency-inverse document frequency ( TFIDF ) representation of the resources and fit appropriate distribution to the data based on the representation used .
We examine supervised learning for multi-class , multi-label text classification . We are interested in exploring classification in a real-world setting , where the distribution of labels may change dynamically over time .
We explore supervised learning for multi-class , multi-label text classification , focusing on real-world settings , where the distribution of labels changes dynamically over time . We use the PULS Information Extraction system to collect information about the distribution of class labels over named entities found in text .
However , the accuracy of centroid-based classifiers is inferior to SVM , mainly because centroids found during construction are far from perfect locations . We design a fast Class-Feature-Centroid ( CFC ) classifier for multi-class , single-label text categorization . In CFC , a centroid is built from two important class distributions : inter-class term index and inner-class term index .
This thesis studies Parametric Mixture Models ( PMMs ) . They are efficient statistical models to solve multi-label text categorization problem . Conventional machine learning models usually training binary classifiers for predicting multi-label problem .
In a joint effort involving federal , state and city business regulating offices , the unification and automation of this codification process is in progress . As the input information is in the form of free textual entries , techniques used in multi-class , multi-label text categorization are well suited . Due to the fact that the possible codes are in the order of one thousand , this problem is under investigation through an approach based on a hierarchical use of Support Vector Machines ( SVM ) .
A number of language-independent text pre-processing techniques , to support multi-class single-label text classification , are described and compared . A simple but effective statistical keyword identification approach is proposed , coupled with a number of phrase identification mechanisms .
For large-scale data such as corpus text , intelligent learning methods have been effectively approached . In this paper , an artificial neural network with a quasi-Newton updating procedure is presented for multi-label multi-class text classification . This numerical unconstrained training technique , the Multi-Label extension of Log-Loss function using in Limited-memory Broyden – Fletcher – Goldfarb – Shanno algorithm ( ML4BFGS ) , provides a noteworthy opportunity for text mining and leads to a significant improvement in text classification performances .
We have further utilized the correlation within the training data to propose a post processing step to refine the probability assigned to each of the test instances by SISC . Empirical evaluation on real world benchmark multi-class and multi-label text data sets and comparison with other state-of-the-art text classification as well as subspace clustering algorithms show that SISC provides superior performance for multi-label text data classification in a multi-class setting .
We skip grid search since it is not feasible over 40 tasks . Although uniform scaling is the norm in the multi - label classification literature , single - task performance is significantly better . Our method outperforms baselines for significant majority of tasks and achieves comparable performance in rest .
The KKT condition for this Lagrangian yields the desired result as section : B Additional Results on Multi - label Classification In this section , we present the experimental results we did not include in the main text .
We thoroughly evaluate it on a number of multilabel classification tasks , and analyze its sensitivity across several parameters . section : Multi - Label Classification To facilitate the comparison between our method and the relevant baselines , we use the exact same datasets and experimental procedure as in [ reference ][ reference ] .
Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods , and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations . We demonstrate the efficacy of node2vec over existing state - of - the - art techniques on multi - label classification and link prediction in several real - world networks from diverse domains . Taken together , our work represents a new way for efficiently learning state - of - the - art task - independent representations in complex networks .
In particular , we design a novel framework , EXplicit interAction Model ( dubbed as EXAM ) , equipped with the interaction mechanism . We justified the proposed approach on several benchmark datasets including both multi - label and multi - class text classification tasks . Extensive experimental results demonstrate the superiority of the proposed method .
In this work , we present a novel framework named EXAM which employs the interaction mechanism to explicitly compute the word - level interaction signals for the text classification . We apply the proposed EXAM on multi - class and multi - label text classifications . Experiments over several benchmark datasets verify the effectiveness of our proposed mechanism .
RCV1 has 103 topic categories in a hierarchy , and one document may be associated with more than one topic . Performance on this task ( multi - label categorization ) is known to be sensitive to thresholding strategies , which are algorithms additional to the models we would like to test . Therefore , we also experimented with single - label categorization to assign one of 55 second - level topics to each document to directly evaluate models .
The model includes an image analysis part and a caption generation part . In the image analysis part , we first use supervised learning to predict a set of attributes , based on words commonly found in image captions We solve this as a multi - label classification problem and train a corresponding deep CNN by minimizing an element - wise logistic loss function . Secondly , a fixed length vector is created for each image , whose length is the size of the attribute set .
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 ) . Some of these binary text examples are subtle .
This signature ensures the implementation of numerous applications for managing effectively a corpus or collections of books ( e.g. information retrieval in digital libraries according to several criteria , or page categorization ) . To illustrate the effectiveness of the proposed page signature , a detailed experimental evaluation has been conducted in this article for assessing two possible categorization applications , unsupervised page classification and page stream segmentation .
In the case of multi-page documents , the preservation of document contexts is a major requirement . To facilitate workflows involving very large amounts of paper scans , page stream segmentation ( PSS ) is the task to automatically separate a stream of scanned images into coherent multi-page documents . In a digitization project together with a German federal archive , we developed a novel approach for PSS based on convolutional neural networks ( CNN ) .
In this manuscript we propose a novel method for jointly page stream segmentation and multi-page document classification . The end goal is to classify a stream of pages as belonging to different classes of documents .
Towards role-based filtering of disease outbreak reports Timeline adaptation for text classification Learning social networks using multiple resampling method Geodata supported classification of patent applications Image modality classification: a late fusion method based on confidence indicator and closeness matrix The Method of Web Text Classification of Using Non-labeled Training Sample On sparse linear discriminant analysis algorithm for high-dimensional data classification Network problems detection and classification by analyzing syslog data A Comprehensive Study on Text Information Extraction from Natural Scene Images A Fuzzy Matching based Image Classification System for Printed and Handwritten Text Documents Genetic Optimization of Keyword Subsets in the Classification Analysis of Authorship of Texts Corrigendum: Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine Intrinsic Plagiarism Detection Approach Utilizing Support Vector Machine in Text Classification Research on Assessment and Optimization Model Based on Text Classification Practical Text Classification With Large Pre-Trained Language Models Effective Use of Word Order for Text Categorization with Convolutional Neural Networks A C-LSTM Neural Network for Text Classification Bag of Tricks for Efficient Text Classification Distributed Representations of Sentences and Documents Squeezed Very Deep Convolutional Neural Networks for Text Classification R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Fuzzy Ontology-Based Approach for Automatic Construction of User Profiles N-Gramas de Caractere como Técnica de Normalização Morfológica para Língua Portuguesa: Um Estudo em Categorização de Textos (Character N-grams as a Morphological Normalization Technique for Portuguese Language: A Study in Text Categorization) Using Unlabeled Data to Improve Author Identification A study in rule-specific issue categorization for e-rulemaking The improved features selection for text classification Methods of Feature Weighting Ascertainment Based on Text Categorization The Factors of Evaluation Text Categorizing Model Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis A New Two-Stage Approach to the Multiaspect Text Categorization The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification Concept based retrieval and information filtering Towards the Automatic Classification of Documents in User-generated Classifications COMPARATIVE STUDY OF CLASSIFICATION ALGORITHM FOR TEXT BASED CATEGORIZATION A Text Categorization Technique based on a Numerical Conversion of a Symbolic Expression and an Onion Layers Algorithm Cloud-based classification of text documents using the Gridgain platform Machine learning based text analysis for intelligence collation Improved sequence generation model for multi-label classification via CNN and initialized fully connection A Multi-label Classification Approach for ICT Fault Text Analysis Recommending Tags for New Resources in Social Bookmarking Systems Supervised Classification Using Balanced Training Improving Supervised Classification Using Information Extraction A class-feature-centroid classifier for text categorization Advanced Parametric Mixture Model for Multi-Label Text Categorization Semi-automatic Codification of Economical Activities with Support Vector Machines Text Classification using Language-independent Pre-processing Multi-label Log-Loss function using L-BFGS for document categorization Clustering guided multi-label text classification Multi-Task Learning as Multi-Objective Optimization DeepWalk: online learning of social representations node2vec: Scalable Feature Learning for Networks Explicit Interaction Model towards Text Classification Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Practical Text Classification With Large Pre-Trained Language Models A structural signature based on texture for digitized historical book page categorization Multi-modal page stream segmentation with convolutional neural networks Deep Neural Networks for Page Stream Segmentation and Classification
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