Search Results for author:
Found 9 papers, 8 papers with code
Czert – Czech BERT-like Model for Language Representation
This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures.
Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task.
Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Linear Transformations for Cross-lingual Sentiment Analysis
This paper deals with cross-lingual sentiment analysis in Czech, English and French languages.
Czech Dataset for Cross-lingual Subjectivity Classification
Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa.
Ranked #1 on
Subjectivity Analysis
on Czech Subjectivity Dataset
Are the Multilingual Models Better? Improving Czech Sentiment with Transformers
Our experiments show that the huge multilingual models can overcome the performance of the monolingual models.
Czert -- Czech BERT-like Model for Language Representation
This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures.
UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.}
UWB @ DIACR-Ita: Lexical Semantic Change Detection with CCA and Orthogonal Transformation
In this paper, we describe our method for detection of lexical semantic change (i. e., word sense changes over time) for the DIACR-Ita shared task, where we ranked $1^{st}$.
