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Found 7 papers, 3 papers with code
Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction
In this work we put forward to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation Extraction task in natural language processing.
m-Networks: Adapting the Triplet Networks for Acronym Disambiguation
Acronym disambiguation (AD) is the process of identifying the correct expansion of the acronyms in text.
CILex: An Investigation of Context Information for Lexical Substitution Methods
Methods based on lexical resources are likely to miss relevant substitutes whereas relying only on contextual word embedding models fails to provide adequate information on the impact of a substitute in the entire context and the overall meaning of the input.
Realistic Counterfactual Explanations with Learned Relations
Many existing methods of counterfactual explanations ignore the intrinsic relationships between data attributes and thus fail to generate realistic counterfactuals.
Image segmentation of cross-country scenes captured in IR spectrum
We suggest the Speeded-Up Robust Features as a basis for our salient features for a number of reasons discussed in the paper.
A movie genre prediction based on Multivariate Bernoulli model and genre correlations
Furthermore, movie ratings are crucial for recommendation engines that track the behavior of all users and utilize the information to suggest items they might like.
A multinomial probabilistic model for movie genre predictions
We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie.
