Распределительные модели

A major focus of current work in distri- butional models of semantics is to con- struct phrase representations composition- ally from word representations. However, the syntactic contexts which are modelled …...

AbstractWe introduce a new approach to semantics which combines the benefits of distributional and formal logical semantics. Distributional models have been successful in modelling the meanings of content words, but …...

AbstractThis paper empirically evaluates the performances of different state-of-the-art distributional models in a nominal lexical semantic classification task. We consider models that exploit various types of distributional features, which thereby …...

A range of approaches to the representa- tion of lexical semantics have been explored within Computational Linguistics. Two of the most popular are distributional and knowledge- based models. This paper …...

Distributional models are derived from co- occurrences in a corpus, where only a small proportion of all possible plausible co- occurrences will be observed. This results in a very sparse …...

Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or co- occurrence features, and train a model for each polysemous word individually. In this work, …...

AbstractThis article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute …...

AbstractIn this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and …...

AbstractIn this paper we focus on patterns of colexification (co-expressions of form-meaning mapping in the lexicon) as an aspect of lexical-semantic organization, and use them to build large scale synset …...