Babonnaud et al. (2016) introduce a framework for modeling systematic polysemy that combines Lexicalized Tree Adjoining Grammar (LTAG) with frame semantics and Hybrid Logic (HL). The components of the syntax-semantics interface are elementary LTAG trees paired with frame descriptions that are expressed by possibly underspecified HL formulas. A brief review of this framework will be given in Sec...

Word embedding has been widely studied and proven helpful in solving many natural language processing tasks. However, the ambiguity of natural language is always a problem on learning high quality word embeddings. A possible solution is sense embedding which trains embedding for each sense of words instead of each word. Some recent work on sense embedding uses context clustering methods to dete...

The article deals with the study of influence interlingual interference in practice teaching Ukrainian language a Turkish-speaking audience. research was carried out on material typical graphic, phonetic and lexical errors, selected as result long-term observations speech students who begin to learn foreign (levels A-1, А-2, B-1). Attention is focused systemic, structural (graphic, phonetic, le...

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived me...

We present a scalable approach to automatically discovering particular objects (as opposed to object categories) from a set of images. The basic idea is to search for local image features that consistently appear in the same images under the assumption that such co-occurring features underlie the same object. We first represent each image in the set as a set of visual words (vector quantized lo...

This paper focuses on verb sense disambiguation cast as inferring the VerbNet class to which a verb belongs. To train three different supervised learning models –Maximum Entropy (MaxEnt), Naive Bayes and Decision Tree– we used lexical, co-occurrence and typed-dependency features. For each model, we built three classifiers: one single classifier for all verbs, one single classifier for polysemou...

The concept of ‘Performance’ is one the most used words, both in academic and professional spheres, due to its importance all fields. In addition very high frequency use, definition polysemous. This paper aims focus on surrounding performance, by listing several definitions tracing evolution over time. also proposes treatment performance facets, from financial global sustainable one, highlighti...

The task of word sense disambiguation aims to select the correct sense of a polysemous word in a given context. When applied to machine translation, the correct translation in the target language must be selected for a polysemous lexical item in the source language. In this paper, we present work in progress on a supervised WSD system with a hybrid approach: on the one hand it relies on supervi...