Model uses RNN and CNN for sequence-based short-text classification.
problem Lack of sequence consideration in short-text classification.
method Recurrent and Convolutional Neural Networks for sequence data.
result Achieves state-of-the-art results on three datasets.
A graph model improves short text classification by integrating sentence relationships.
problem Sparse features in short text classification due to limited text length.
method PathWalk model combining graph networks and short sentences.
result PathWalk achieves state-of-the-art results on four datasets.
New Gamma-Poisson model improves topic selection for short text.
problem Topic modelling for short text using Poisson distribution.
method Gamma-Poisson mixture model with collapsed Gibbs sampler.
result Gamma-Poisson model selects more accurate number of topics.
Paper introduces a new text clustering model using Beta-Liouville priors.
problem Clustering short text data.
method Develops a hierarchical mixture model with Beta-Liouville priors for short text clustering.
result The Beta-Liouville distribution offers a more flexible correlation structure for short text clustering.
Large dataset for medical abstracts classified by sentence role.
problem Efficiently classify long medical abstracts for researchers.
method Labeled dataset of 200k abstracts, each with 2.3M sentences.
result Improved sentence classification for medical literature.
SoulMate links short texts through multi-aspect embeddings.
problem Linking authors of noisy, ambiguous short texts.
method Neural network-based temporal-textual framework generating author subgraphs.
result Higher performance in linking short-text authors compared to competitors.
POTA improves short text clustering by generating reliable pseudo-labels.
problem Limited discriminative representations in short texts.
method POTA uses instance-level attention and optimal transport for semantic consistency and cluster structure.
result POTA outperforms state-of-the-art methods in short text clustering.
BBM models short texts using biterms to improve coherence.
problem Challenges in analyzing short texts from social media.
method Bag of Biterms (BoB) for document representation and simple statistical models.
result BBM enhances coherence and performance over traditional models.
New algorithm improves topic modeling for short texts.
problem Inference of latent topics from short texts is challenging.
method Developed a stochastic divergence minimization algorithm for Biterm Topic Model (BTM).
result The new algorithm estimates latent topics more accurately and efficiently.
AOBTM adapts online topic modeling for short app reviews, revealing coherent topics over time.
problem Challenges in inferring latent topics from short, dynamic app reviews over multiple versions.
method Adaptive Online Biterm Topic Model (AOBTM) that addresses sparsity and considers statistical data from previous versions.
result AOBTM finds more coherent topics and outperforms state-of-the-art baselines.
Paper improves short text clustering by integrating semantic relationships into Optimal Transport.
problem Erroneous pseudo-labels caused by neglecting semantic consistency in existing OT methods.
method Designs an instance-level attention mechanism to capture semantic relationships and integrates them into the OT formulation.
result Generates reliable pseudo-labels that improve clustering accuracy.
Model clusters authors and topics in short texts like social media posts.
problem Analysis of short texts is difficult due to brevity and lack of context.
method Expands Latent Dirichlet Allocation to model word dependencies and cluster users.
result Improves topic and user clustering, outperforming traditional methods.
Study examines how cluster number affects short-text clustering, introducing a stability metric.
problem Challenges in finding meaningful clusters in short-text data.
method Introduces a stability metric to determine cluster robustness and visualizes cluster subdivisions.
result Choosing a cluster number involves balancing informativeness and complexity, not seeking a single 'optimal' solution.
Deep-learning model detects personality traits from short texts across multiple languages.
problem Recognizing author personality traits from short texts.
method Uses deep-learning-based models and atomic features of text to build hierarchical, vectorial word and sentence representations.
result Shows state-of-the-art performance across five traits and three languages (English, Spanish, and Italian).
Paper proposes deep learning for fake claim detection on social media.
problem Spread of false information on social media.
method Extended LIAR dataset with sentiment analysis; BERT-Base model for classification.
result 70% accuracy in classifying claims as genuine or fake.
This research creates and classifies datasets for Setswana and Sepedi news headlines.
problem Challenges in creating and preparing datasets for low-resourced languages.
method Investigates an approach for data augmentation tailored to low resource languages.
result Improves classification performance on news topic classification task.
The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective and efficient topic modeling approaches. While latent Dirichlet allocation (LDA) can be applied, it is not optimal due to its weakness in handling short texts with fast-changing topics…
New algorithm clusters sparse, high-dimensional texts efficiently.
problem Clustering very short texts with high dimensions and sparsity.
method Linear algebra-based subspace clustering algorithm.
result Algorithm performs competitively on text categorization tasks.
This paper discusses sentiment analysis on social media text.
problem Detecting sentiments in short text messages for applications like mental health monitoring.
method Combines concepts from Natural Language Processing and Machine Learning.
result Explains techniques used in sentiment analysis of textual data.
Framework incorporates prior knowledge into Bayesian models for data streams.
problem Effective use of prior knowledge in learning Bayesian models from streaming data.
method Proposes a novel framework that subsumes existing models for time-series data.
result Framework outperforms existing methods with a large margin.
This paper predicts legal proceedings status using NLP and machine learning.
problem Classify Brazilian legal proceedings into archived, active, and suspended categories.
method Combined NLP techniques with machine learning to classify legal proceedings sequences.
result Achieved maximum accuracy of 93% and top average F1 Scores of 89% (macro) and 93% (weighted).
A new method for semi-supervised text classification using layer partitioning.
problem Adapting neural semi-supervised learning to discrete text inputs.
method Decompose neural network into feature extractor F and update layer U for training. Use dropout for systematic noise. result Improves text classification especially on short texts compared to state-of-the-art methods.
Method selects optimal source domain for better cross-domain sentiment classification.
problem Improving sentiment classification on unseen domains with limited labeled data.
method Predictive measure based on distance functions between source and target domains.
result Significant improvement in cross-domain classification error compared to random selection.
Interactive learning improves real-time tweet classification for situational awareness.
problem Difficulty in identifying relevant tweets from noisy social media data.
method Interactive learning framework that incorporates user feedback in real-time.
result Our approach outperforms state-of-the-art models in real-time tweet classification.
jLDADMM offers Java tools for topic modeling on texts.
problem Topic modeling on texts, especially short texts.
method Collaborated Gibbs sampling for LDA and DMM models.
result Alternative topic modeling solutions for normal or short texts.
GCAN detects fake news on social media with explanations.
problem Detecting fake news on social media with explanations.
method Graph-aware Co-Attention Networks (GCAN).
result GCAN significantly outperforms state-of-the-art methods in accuracy.
This study improves sentence embeddings from BERT models.
problem Capturing the underlying meaning of sentences using BERT models.
method Comprehensive review and testing of various sentence embedding extraction and refinement methods.
result Representation-shaping techniques significantly improve sentence embeddings from BERT-based and simple baseline models.
SCROLLS benchmarks long text NLP tasks, improving existing models.
problem Short NLP benchmarks ignore long texts; SCROLLS addresses this.
method Handpicked long-text datasets for summarization, QA, and inference tasks.
result Improvement potential on SCROLLS tasks, as indicated by initial baselines.
Study finds strong link between crypto narratives and prices.
problem Understanding the impact of crypto narratives on prices.
method Topic modeling of Twitter data combined with sentiment analysis.
result Strong correlation between narratives and crypto prices.
Guidelines for using explainable ML to avoid misuse.
problem Misuse of explainable ML, especially for harmful purposes.
method Proposed guidelines to promote best practices.
result Promote interpretable models and testing methods.
Paper introduces new indicators for forecasting crude oil prices using short news headlines.
problem Forecasting crude oil prices from short, noisy news headlines using LDA.
method Developed two novel indicators for topic and sentiment from short text data, and applied AdaBoost.RT.
result AdaBoost.RT with the proposed indicators outperforms benchmarks in crude oil forecasting.
A new neural topic model using optimal transport improves document representation and topic coherence.
problem Challenges in achieving good document representation and coherent/diverse topics in existing NTMs.
method Proposes a neural topic model via optimal transport, learning topic distribution by minimising OT distance to document word distributions.
result Significantly outperforms state-of-the-art NTMs on discovering coherent and diverse topics.
Generative grading improves automated feedback for structured problems.
problem Difficulty in providing high-quality feedback on structured assignments.
method Generative descriptions of student cognition, probabilistic programs, and learning to infer feedback.
result Achieved near-human accuracy in grading and feedback across diverse domains.
Deep learning classifies railroad accident causes from narrative reports.
problem Classifying accident causes from narrative reports is challenging.
method Applied deep learning with word embeddings to classify accident causes.
result Deep learning accurately classifies accident causes from narratives and identifies inconsistencies.
GCTM integrates GCN into topic models for better topic learning from data streams.
problem Learning hidden topics from short and noisy data streams with concept drift.
method Proposes a graph convolutional topic model (GCTM) that learns from a knowledge graph and old data.
result Significantly better performance in probabilistic predictive measures and topic coherence.
Paper presents a new framework for sequence classification.
problem Sequence classification in real-world applications.
method Reference-based sequence classification framework.
result New sequence classification algorithms achieve comparable accuracy.
Dual-stage sEMG classification improves gesture recognition accuracy.
problem Improving accuracy in hand gesture recognition from sEMG signals.
method Dual-stage classification approach: first stage groups similar activities, second stage classifies within groups.
result Dual-stage classification yields significantly higher accuracy than single-stage approach.
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
problem Avoid risky misclassification in error-critical applications.
method Learning an ensemble of cost-sensitive classifiers.
result Improved classification accuracy and flexibility in loss selection.
The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
Paper introduces LPCs for robust classification with performance bounds.
problem Conventional classification techniques constrain rules and use surrogate losses.
method Robust risk minimization (RRM) for unconstrained classification rules, optimizing 0-1 loss.
result LPCs provide performance bounds and competitive performance with state-of-the-art techniques.
New NHCAs improve multi-category classification efficiency.
problem Efficient multi-category classification for real-world problems.
method Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM), and Improved GEPSVM (IGEPSVM) with OAA, BT, and TDS approaches.
result TDS-TWSVM outperforms other methods in classification accuracy.
Paper compares XGB and BPNN for music style classification.
problem Efficient music style classification using different methods.
method Feature extraction for timbral texture, rhythmic content, and pitch content; comparative evaluation of XGB and BPNN.
result XGB outperforms BPNN for small datasets in music classification.
Develops algorithms for constructing statistical industry classifications.
problem Creating accurate industry classifications for quantitative trading.
method Combines clustering algorithms with correlation analysis to improve fundamental industry classifications.
result Shows that clustering methods improve off-the-shelf industry classifications.
Deep reinforcement learning improves classification accuracy for imbalanced datasets.
problem Imbalanced datasets challenge conventional classification algorithms.
method Formulated as a sequential decision-making process, solved using deep Q-learning network.
result Proposed model outperforms other imbalanced classification algorithms.
Classifies Lie algebra realizations by vector fields.
problem Classifying Lie algebra realizations by vector fields.
method Generalized correspondence between classification of transitive local realizations and subalgebras, formulated a reasonable classification problem, presented an algorithm for construction.
result Algorithm for constructing classification of general realizations.
Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.
problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.