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Found 11 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.
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations
And we propose an ABSA-specific augmentation method to create such augmentations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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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).
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.
Channel-by-Channel Demosaicking Networks with Embedded Spectral Correlation
Although the total size of our model is significantly smaller than the state of the art demosaicking networks, it achieves substantially higher performance in both demosaicking quality and computational cost, as validated by extensive experiments.
Topic representation: finding more representative words in topic models
The top word list, i. e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models.
Low Cost Edge Sensing for High Quality Demosaicking
Compared to methods of similar computational cost, our method achieves substantially higher accuracy, Whereas compared to methods of similar accuracy, our method has significantly lower cost.
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs
Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space.
