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.
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying …
We apply text analysis approaches for a specialized search engine for 3D CAD models and associated products. The main goals are to distinguish between actual product descriptions and other text on a website, as well as to decide whether a given text is or contains a product name. For this we use paragraph vectors for t…
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.
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.
We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following …
Dual-attention GCN improves text classification by adapting to textual complexity.
problem Challenges in learning discriminative features from texts due to graph variants.
method Proposes a dual-attention GCN with connection-attention and hop-attention mechanisms.
result Achieves state-of-the-art performance on text classification tasks.
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.
Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in the text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed…
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.
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.
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.
Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional tex…
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.
In text mining, information retrieval, and machine learning, text documents are commonly represented through variants of sparse Bag of Words (sBoW) vectors (e.g. TF-IDF). Although simple and intuitive, sBoW style representations suffer from their inherent over-sparsity and fail to capture word-level synonymy and polyse…
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.
We give a short proof of a theorem of Handel and Mosher stating that any finitely generated subgroup of Out(FN) either contains a fully irreducible automorphism, or virtually fixes the conjugacy class of a proper free factor of FN, and we extend their result to non finitely generated subgroups of $\text{Ou…
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.
The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suit…
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.
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatica…
Paper proposes HGAT for detecting fake news on HIN.
problem Viral spread of fake news causing social harm.
method Hierarchical Graph Attention Network (HGAT) for node representation learning in HIN.
result HGAT outperforms text-based and network-based models.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed. Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new contents. Unlike conventional topic models such as latent Dirichle…
System identifies language of transliterated text.
problem Users struggle to understand non-native language transliterated text.
method Feature extraction of phonetic syllables using LSTM network.
result System accurately identifies language of transliterated text.
Deep learning models outperform classical methods in text classification.
problem Improving text classification accuracy using deep learning.
method Comprehensive review of deep learning models and datasets for text classification.
result Deep learning models outperform classical methods on various text classification tasks.
Survey of deep learning for Hindi text classification.
problem Limited research on morphologically rich, low-resource Hindi text classification.
method Comparison of CNN, LSTM, Transformer, BERT, and LASER for Hindi text classification.
result Multilingual pre-trained sentence embeddings outperform traditional architectures for Hindi text classification.
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).
In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both hi…
Developed text classification system for Azerbaijani language.
problem Text clustering problem in Azerbaijani language.
method Machine learning and embedding techniques.
result System successfully categorizes news, product reviews, and more.
Most of the information is stored as text, so text mining is regarded as having high commercial potential. Aiming at the semantic constraint problem of classification methods based on sparse representation, we propose a weighted recurrent neural network (W-RNN), which can fully extract text serialization semantic infor…
Criminals use malware to disrupt cyber-systems. The number of these malware-vulnerable systems is increasing quickly as common systems, such as vehicles, routers, and lightbulbs, become increasingly interconnected cyber-systems. To address the scale of this problem, analysts divide malware into classes and develop, for…
Enhances medical code predictions for multi-morbidity patients using text classification.
problem Improving accuracy in predicting medical codes for patients with multiple illnesses.
method Used machine learning techniques, including multi-label medical text classification, to enhance predictions.
result High dimensional embeddings pre-trained on health data significantly improve multi-label classification performance.
Combines BERT and graph CNN for improved text classification.
problem Text classification problems
method Combining BERT embedding and graph convolutional neural network
result Graph CNN model performs better than classical models combined with BERT
The paper classifies actions of a specific group on certain manifolds.
problem Classifying analytic actions of a specific semi-orthogonal group on manifolds.
method Adapting Uchida's construction, the paper explicitly constructs actions on specific manifolds and demonstrates that any action is covered by these.
result Any analytic action of the semi-orthogonal group on a closed, connected manifold is covered by the constructed actions.
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.
Mitigates bias in text classification by weighting instances.
problem Unintended biases in text classification datasets based on demographic terms.
method Instance weighting to recover non-discrimination distribution.
result Effective mitigation of unintended biases without sacrificing generalization.
Improves video search by balancing text and visual modalities.
problem Modality imbalance in video search models, focusing mainly on text matching.
method Proposes MBVR with MS samples and DM to balance modalities.
result Empirically shows significant improvement in modality balance and search effectiveness.
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.
HyperBERT enhances BERT for node classification on text-attributed hypergraphs.
problem Challenges in capturing hypergraph structure and text attributes in node classification.
method Mixing hypergraph-aware layers with BERT for improved node classification.
result HyperBERT achieves state-of-the-art results on text-attributed hypergraph benchmarks.
PySS3 simplifies access to SS3's text classification and visualization.
problem Limited availability of an open-source SS3 implementation.
method Developed PySS3, an open-source Python package implementing SS3.
result PySS3 enables robust, explainable, and trusty text classification.
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…
Enhanced word embeddings boost multiclass text classification accuracy.
problem Improving multiclass text classification accuracy using pre-trained embeddings.
method Proposed word-class embeddings (WCEs) to enhance pre-trained word embeddings.
result WCEs significantly improve multiclass text classification accuracy.
Paper defends LSTM-based text classification models from backdoor attacks.
problem Backdoor attacks in LSTM models cause misclassification of spam or malicious speech.
method Backdoor Keyword Identification (BKI) to identify and exclude poisoned samples.
result BKI method effectively mitigates backdoor attacks in various text classification datasets.
Text classification on drug SMILES strings yields competitive drug type classification results.
problem Classifying drug types using conventional text classification methods.
method Treated drug SMILES as sentences and applied basic NLP methods for classification.
result Competitive drug type classification results achieved.
Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Althou…
Combines BERT and GCN for better text classification.
problem Limited global information capture by BERT.
method Integrates BERT with VGCN for improved text classification.
result VGCN-BERT outperforms BERT and GCN alone.