no code implementations • NAACL (ClinicalNLP) 2022 • Sandaru Seneviratne, Elena Daskalaki, Artem Lenskiy, Hanna Suominen
Acronym disambiguation (AD) is the process of identifying the correct expansion of the acronyms in text.
1 code implementation • COLING 2022 • Sandaru Seneviratne, Elena Daskalaki, Artem Lenskiy, Hanna Suominen
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.
1 code implementation • ACL 2022 • Andrea Papaluca, Daniel Krefl, Hanna Suominen, Artem Lenskiy
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.
no code implementations • 15 Feb 2022 • Xintao Xiang, Artem Lenskiy
Many existing methods of counterfactual explanations ignore the intrinsic relationships between data attributes and thus fail to generate realistic counterfactuals.
no code implementations • 8 Apr 2016 • Artem Lenskiy
We suggest the Speeded-Up Robust Features as a basis for our salient features for a number of reasons discussed in the paper.
no code implementations • 25 Mar 2016 • Eric Makita, Artem Lenskiy
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.
1 code implementation • 25 Mar 2016 • Eric Makita, Artem Lenskiy
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.