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Found 8 papers, 2 papers with code
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts
Short text nowadays has become a more fashionable form of text data, e. g., Twitter posts, news titles, and product reviews.
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms
To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator.
Learning with Partial Labels from Semi-supervised Perspective
Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP).
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence.
Aspect-Based Sentiment Analysis (ABSA)
Contrastive Learning
+2
Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals
The recent generative dataless methods construct document-specific category priors by using seed word occurrences only, however, such category priors often contain very limited and even noisy supervised signals.
Variational Wasserstein Barycenters with c-Cyclical Monotonicity
The basic idea is to introduce a variational distribution as the approximation of the true continuous barycenter, so as to frame the barycenters computation problem as an optimization problem, where parameters of the variational distribution adjust the proxy distribution to be similar to the barycenter.
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning
In this paper, we propose a novel PU learning method, namely Positive and unlabeled learning with Partially Positive Mixup (P3Mix), which simultaneously benefits from data augmentation and supervision correction with a heuristic mixup technique.
Semi-Supervised Text Classification with Balanced Deep Representation Distributions
They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts.
