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Found 8 papers, 1 papers with code
Domain Representative Keywords Selection: A Probabilistic Approach
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain.
Can a simple approach identify complex nurse care activity?
For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e. g., data from motion capture, accelerometer, and gyroscopes sensors.
Exploring Variational Graph Auto-Encoders for Extract Class Refactoring Recommendation
Then, the variational graph auto-encoder is used to learn a vector representation for each method.
Coordinated Topic Modeling
It then uses the axes to model a corpus for easily understandable representation.
Unsupervised Open-domain Keyphrase Generation
To solve this problem, we propose a seq2seq model that consists of two modules, namely \textit{phraseness} and \textit{informativeness} module, both of which can be built in an unsupervised and open-domain fashion.
TopicAdapt- An Inter-Corpora Topics Adaptation Approach
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus.
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling
Besides, we provide a simple solution extending a neural topic model to reduce the effect of noisy out-of-topics text generation from PLMs.
Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach
From an extensive experiment on both an open domain and a technical domain QA dataset, we find that our model outperforms the state-of-the-art models on various textual and factual metrics for the LFQA task.
