A new method learns text network embeddings by combining generative autoencoder and homophilic priors.
problem Improving performance of network learning applications, especially for textual networks.
method Variational Homophilic Embedding (VHE) - a fully generative model that optimizes a variational autoencoder for semantic information and a homophilic prior for structural information.
result VHE outperforms existing methods in various tasks on real-world textual networks.
Dynamic Embedding learns text node representations in evolving graphs.
problem Learning text node embeddings in dynamic graphs.
method DetGP model using Gaussian process for non-parametric structure learning.
result DetGP efficiently updates embeddings for dynamic graphs without re-training.
DMTE integrates global connectivity for better text embeddings.
problem Lack of capturing complete connectivity between texts in graphs.
method Integrates global structural information through diffusion-convolution on text inputs, preserving high-order proximity.
result DMTE outperforms state-of-the-art methods on vertex-classification and link-prediction tasks.
The study finds that supply chain information from LLM embeddings improves stock returns predictions.
problem Predicting stock returns using textual information from annual reports.
method Combining LLM embeddings of annual reports with supply chain knowledge graph propagation.
result Network-augmented embeddings significantly predict stock returns with a Sharpe ratio of 0.86 and alpha of 7.27%.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.
Paper tackles zero-shot activity recognition using video features and text embeddings.
problem Zero-shot activity recognition with videos.
method Auto-encoder model for multimodal joint embedding, 3D convolutional action recognition for visual features, GloVe word embeddings for textual features.
result Improved zero-shot recognition results with top-n accuracy and mean Nearest Neighbor Overlap.
Researchers develop hyperbolic neural networks for improved data embedding.
problem Lack of hyperbolic neural network layers limits the use of hyperbolic embeddings in machine learning.
method Combining Möbius gyrovector spaces and Riemannian geometry of the Poincaré model to derive hyperbolic neural network layers.
result Hyperbolic sentence embeddings outperform or match Euclidean variants on textual entailment and noisy-prefix recognition tasks.
Method retrieves similar fashion items from images and text, enabling style refinement.
problem Lack of intuitive, interactive refinement in search engines for fashion items.
method Joint visual-textual embedding training, Mini-Batch Match Retrieval, attribute extraction.
result Improved performance in multimodal style search, demonstrated through benchmark.
Textual data predicts electricity consumption and weather.
problem Lack of textual data in time series prediction models.
method Used TF-IDF and neural word embeddings to predict time series from text.
result Textual data can predict time series with sufficient accuracy.
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.
GEMRank embeds users and items using co-occurrence relations for better collaborative filtering.
problem Lack of textual data for entity embedding in recommender systems.
method Uses profile co-occurrence for entity relations and factorization for embedding. Feeds embeddings into a neural network for predictions.
result Significantly outperforms baseline algorithms in various data sets.
SFBoW provides sentence embeddings with predefined dimensions.
problem Sentence embeddings problem at document-level.
method Refinement of Fuzzy Bag-of-Words, predefined dimension.
result Competitive performances in Semantic Textual Similarity benchmarks.
This paper reviews different word embeddings for sentiment classification using deep learning.
problem Handling large textual data with simple ML algorithms.
method Word embedding strategies implemented on an Amazon Review Dataset.
result Different word embeddings improve accuracy in sentiment classification.
Paper debiases multiple word embedding biases simultaneously.
problem Reduction of multiple biases in word embeddings.
method Joint multiclass debiasing approach using Word Embeddings Association Test (WEAT).
result Demonstrates reduction or complete elimination of bias in word embeddings.
Zero-shot audio classification using class label embeddings.
problem Classifying audio without labeled data.
method Bilinear model with audio feature embeddings and class label embeddings.
result Achieved accuracy up to 39.7% for natural audio categories.
Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visu…
This work shows cosine similarity is equivalent to Pearson correlation for word vectors, but not all vectors are suitable for cosine.
problem The use of cosine similarity for semantic textual similarity is often taken for granted, despite its limitations.
method Characterized cases where Pearson correlation is unfit and introduced rank correlation as an alternative.
result Pearson correlation is equivalent to cosine similarity for many word vectors but not all, and rank correlation can improve performance.
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio…
Deep neural networks justify medical diagnoses with textual explanations.
problem Improving machine learning in medical diagnosis justification.
method Mapping X-Ray images to textual representations, generating explanations, and multi-task training.
result The method significantly outperforms existing justification methods and achieves high accuracy.
Improved financial network predictability using LLM for edge filtering.
problem Spurious edges in financial networks from textual similarity.
method Two-stage framework: sparse candidate graph + LLM edge classification.
result LLM-based edge filtering improves Sharpe ratio and reduces drawdown.
Semi-supervised method enhances recommendation from sparse data.
problem Data sparsity in implicit feedbacks and lack of negative samples.
method Domain adaptation using textual features to align latent spaces.
result Our models significantly outperform existing models on real-world datasets.
Detects radical content on Twitter using textual, psychological, and behavioral signals.
problem Limit the spread of extremist narratives on social media.
method Analyzed extremist material, created contextual text-based model, inferred psychological properties, evaluated on Twitter.
result Radical users exhibit distinguishable textual, psychological, and behavioral properties.
Enhances thematic investing with stock embeddings from textual data.
problem Challenges in constructing thematic portfolios due to overlapping sector boundaries and evolving market dynamics.
method Introduces THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning, aligning themes and stocks using their hierarchical relationship and incorporating stock returns.
result Theme-aligned portfolios demonstrate compelling performance, significantly outperforming large language models in thematic asset retrieval.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
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.
Paper proposes a new method for sentence embeddings using weighted word vectors.
problem Improving sentence embeddings for natural language processing tasks.
method A simple sentence embedding method using weighted average of word vectors followed by soft projection.
result Demonstrates effectiveness on clinical semantic textual similarity task.
A new framework predicts stock movements using news sentiment and relational data.
problem Predicting stock prices from textual information is challenging due to market uncertainty and natural language complexity.
method Multi-Graph Recurrent Network (MGRN) combining textual sentiment from financial news and relational data.
result The model outperforms benchmarks in predicting stock movements.
The paper presents a method to preserve privacy in text analysis using calibrated noise.
problem Accurately learning from user data while maintaining privacy.
method Calibrated multivariate perturbations applied to word embeddings to achieve geo-indistinguishability.
result The method provides better privacy guarantees than baseline models with minimal utility loss.
HTMLPhish detects phishing web pages using deep learning on HTML content.
problem Growing phishing attacks on the web require new detection methods.
method HTMLPhish uses Convolutional Neural Networks (CNNs) to classify HTML documents as phishing or benign.
result HTMLPhish achieves over 93% accuracy in detecting phishing web pages.
A new algorithm learns graph embeddings considering directionality, improving multiple tasks.
problem Lack of directionality in graph embedding algorithms affects performance across tasks.
method DIAGRAM, a multi-objective model that preserves direction, textual features, and graph context.
result DIAGRAM significantly outperforms state-of-the-art baselines on link prediction and node classification.
Enhances GNNs with text features for better fake news detection.
problem Detecting disinformation on social media using GNNs.
method Integrates Transformer-based textual features into GNNs.
result Contextual text representations improve GNN performance by 33.8% in Macro F1.
New method predicts drug interactions from drug images.
problem Predicting drug interactions from molecular structures.
method Siamese neural network using drug structure images.
result First work predicting DDIs from drug images.
In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformatio…
Novel method diagnoses large language models' reasoning abilities.
problem Fine-grained evaluation of large language models' reasoning abilities.
method Adapting cognitive diagnosis models to LLMs, estimating mastery profiles and Q-matrix, incorporating textual information.
result Accurate parameter recovery and insights into LLMs' capabilities.
SAFE detects fake news by analyzing text and image similarities.
problem Detecting fake news with less focus on text-image similarity.
method SAFE uses neural networks to extract text and visual features, then learns their relationship to predict fake news.
result SAFE effectively recognizes fake news based on text, images, or mismatches.
Transformer learns CoVaR from financial news, improving systemic risk forecasts.
problem Quantifying systemic financial risk using conditional Value-at-Risk (CoVaR).
method Transformer-based approach integrating financial news articles with market data.
result Transformer CoVaR improves out-of-sample forecasts and identifies market stress periods.
BERTopic improves financial text analysis with FinTextSim's contextual embeddings.
problem Analyzing financial text data for insights and predictions.
method Integrates BERTopic with FinTextSim for topic modeling and clustering.
result BERTopic performs better with FinTextSim's embeddings, improving topic clarity and reducing misclassification.
Deep Learning predicts e-commerce activity from Italian enterprise websites.
problem Predicting e-commerce activity from Italian enterprise websites.
method Developed a sophisticated processing pipeline using Convolutional Neural Networks and Word Embeddings.
result Deep Learning outperforms traditional Machine Learning methods for text classification.
WME generates document embeddings from word embeddings, outperforming state-of-the-art techniques.
problem Lack of unsupervised document embeddings from pre-trained word embeddings.
method Word Mover's Embedding (WME) approach.
result WME consistently matches or outperforms state-of-the-art techniques on various text classification and similarity tasks.
Framework quantifies semantic similarity between groups of embeddings.
problem Quantifying semantic similarity between groups of embeddings.
method Formulates model comparison task, contrasts generative models, uses information criteria.
result Achieves competitive results in Semantic Textual Similarity tasks.
Paper introduces a benchmark for predicting bankruptcy from text data.
problem Lack of a common benchmark dataset and evaluation strategy for unstructured data in bankruptcy prediction.
method Describes and evaluates several baseline models, including a bag-of-words model.
result A lightweight bag-of-words model performs surprisingly well, especially when considering data from multiple years.
A model integrates CNN and LSTM with LLM for better stock forecasting.
problem Complex stock market prediction with volatile patterns and multifaceted data.
method Two-Level Conv-LSTM Neural Network with LLM for holistic stock advising.
result Improved prediction accuracy and contextual insights.
LMoE uses LLMs to improve stock trading by selecting experts based on textual and price data.
problem Traditional neural network-based router selection in MoE models is suboptimal and ignores textual data.
method Proposes LLMoE, using LLMs as routers to select experts based on historical price data and stock news.
result LLoM outperforms state-of-the-art MoE models and other deep neural network approaches.
Urban2Vec combines street view imagery and POIs for better urban neighborhood embeddings.
problem Lack of comprehensive representation of urban neighborhoods using heterogeneous data.
method Unsupervised multi-modal framework using CNN for visual features and bag-of-words for POI data.
result Urban2Vec achieves better performance than baseline models and comparable to fully-supervised methods.
A new topology design improves zero-shot classification performance in contrastive learning.
problem Improving zero-shot classification performance in contrastive visual-textual alignment.
method Proposed an alternative topology design using multiple class tokens and an oblique manifold with negative inner product.
result Improves zero-shot classification performance by an average of 6.1%.
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for mo…
A novel method extracts topological features from word embeddings for text classification.
problem High dimensional and noisy text representations in natural language processing.
method Persistent homology for topological data analysis on word embeddings.
result Topological features outperform conventional text mining features on long textual documents.
This paper uses news sentiment to predict stock prices and profits.
problem Predicting stock prices using news sentiment.
method Supervised and reinforcement learning for automated decision-making.
result News sentiment can be used to predict stock returns.