Papers Text Categorization
“Text Categorization” 태그가 달린 논문 247편 · 필터 해제
A Hidden Variables Approach to Multilabel Logistic Regression
Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden v…
ClassificationGeneral ClassificationregressionText CategorizationA Comparative Study of Pretrained Language Models on Thai Social Text Categorization
The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the…
General ClassificationLanguage ModelingLanguage ModellingText CategorizationImproving Document Classification with Multi-Sense Embeddings
Efficient representation of text documents is an important building block in many NLP tasks. Research on long text categorization has shown that simple weighted averaging of word vectors for sentence representation often…
ClassificationClusteringDocument ClassificationGeneral Classification+3Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRetrievalText Categorizationt-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detect…
Anorexia DetectionClassificationDocument ClassificationGeneral Classification+4Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Self-supervised pre-training of transformer models has shown enormous success in improving performance on a number of downstream tasks. However, fine-tuning on a new task still requires large amounts of task-specific lab…
DiversityEntity TypingFew-Shot LearningGeneral Classification+7IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis
Legal Tech is developed to help people with legal services and solve legal problems via machines. To achieve this, one of the key requirements for machines is to utilize legal knowledge and comprehend legal context. This…
Natural Language InferenceQuestion AnsweringText CategorizationEnsemble Quantile Classifier
Both the median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. But these methods are restricted as they assign equal weight to e…
Text CategorizationRecognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers
In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably u…
Deep LearningFace RecognitionHandwritten Digit RecognitionSentence+4Sequential Learning of Convolutional Features for Effective Text Classification
Text classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this task. However, CNNs which we…
ClassificationGeneral ClassificationSentiment AnalysisText Categorization+2Multi-Granular Text Encoding for Self-Explaining Categorization
Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classi…
Text CategorizationTowards Unsupervised Text Classification Leveraging Experts and Word Embeddings
Text classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach ad…
ClassificationGeneral ClassificationText Categorizationtext-classification+3Text Categorization by Learning Predominant Sense of Words as Auxiliary Task
Distributions of the senses of words are often highly skewed and give a strong influence of the domain of a document. This paper follows the assumption and presents a method for text categorization by leveraging the pred…
Multi-Task LearningText CategorizationLeap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole t…
General ClassificationMachine TranslationQuestion AnsweringSentiment Analysis+3Heterogeneous Multi-task Metric Learning across Multiple Domains
Distance metric learning (DML) plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in target domain is limited, transfer metric learning (TML) helps to learn the met…
Metric LearningScene ClassificationText CategorizationTransfer LearningRep the Set: Neural Networks for Learning Set Representations
In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable…
General ClassificationGraph ClassificationText CategorizationLearning with Inadequate and Incorrect Supervision
Practically, we are often in the dilemma that the labeled data at hand are inadequate to train a reliable classifier, and more seriously, some of these labeled data may be mistakenly labeled due to the various human fact…
image-classificationImage Classificationspeech-recognitionSpeech Recognition+1Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
With the development of graph convolutional networks (GCN), deep learning methods have started to be used on graph data. In additional to convolutional layers, pooling layers are another important components of deep lear…
Text CategorizationTrigonometric comparison measure: A feature selection method for text categorization
Text data represented using vector space model is high dimensional data since the number of words can easily grow to tens of thousands for a moderate sized dataset. It may contain lots of redundant or irrelevant features…
Dimensionality Reductionfeature selectionText CategorizationStructure-Aware Convolutional Neural Networks
Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures. It causes that CNNs are allowed to manage data with Euclidean o…
Action DetectionAction RecognitionActivity DetectionClustering+5