Search Results for author:
Found 10 papers, 6 papers with code
A Network Framework for Noisy Label Aggregation in Social Media
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.
Cultural Vocal Bursts Intensity Prediction
Image Classification
+2
Siamese Network-Based Supervised Topic Modeling
Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification.
Attentional Encoder Network for Targeted Sentiment Classification
Most of the previous approaches model context and target words with RNN and attention.
Ranked #1 on
Sentiment Analysis
on Twitter
Neural Mixed Counting Models for Dispersed Topic Discovery
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
Context Reinforced Neural Topic Modeling over Short Texts
As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities.
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction
Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference
Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure.
Topic Driven Adaptive Network for Cross-Domain Sentiment Classification
In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space.
Nonparametric Forest-Structured Neural Topic Modeling
Neural topic models have been widely used in discovering the latent semantics from a corpus.
