Семантические задачи

Structured distributional semantic models aim to improve upon simple vector space models of semantics by hypothesizing that the meaning of a word is captured more effectively through its relational — …...

. This paper presents a knowledge base containing triples involving pairs of verbs associated with seman- tic or discourse relations. The relations in these triples are marked by discourse connectors …...

AbstractIn transductive learning, an unlabeled test set is used for model training. Although this setting deviates from the common assumption of a completely unseen test set, it is applicable in …...

AbstractStatic and contextual multilingual embeddings have complementary strengths. Static embeddings, while less expressive than contextual language models, can be more straightforwardly aligned across multiple languages. We combine the strengths of …...

AbstractLarge pretrained multilingual models, trained on dozens of languages, have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. Further adapting these models to specific …...

AbstractDense vector representations for textual data are crucial in modern NLP. Word embeddings and sentence embeddings estimated from raw texts are key in achieving state-of-the-art resultsin various tasks requiring semantic …...

AbstractFunctional Distributional Semantics is a linguistically motivated framework for modelling lexical and sentence-level semantics with truth-conditional functions using distributional information. Previous implementations of the framework focus on subjectverbobject (SVO) triples …...