Unsupervised learned representations of polysemous words generate a large of pseudo multi senses since unsupervised methods are overly sensitive to contextual variations. In this paper, we address the pseudo multi-sense detection for word embeddings by dimensionality reduction of sense pairs. We propose a novel principal analysis method, termed ExRPCA, designed to detect both pseudo multi sense...

Manual annotation of natural language to capture linguistic information is essential for NLP tasks involving supervised machine learning of semantic knowledge. Judgements of meaning can be more or less subjective, in which case instead of a single correct label, the labels assigned might vary among annotators based on the annotators’ knowledge, age, gender, intuitions, background, and so on. We...

This paper presents a method to combine two unsupervised methods (Specification Marks, Conceptual Density) and one supervised (Maximum Entropy) for the automatic resolution of lexical ambiguity of nouns in English texts. The main objective is to improved the accuracy of knowledge-based methods with statistical information supplied by the corpus-based method. We explore a way of combining the cl...

Word Sense Disambiguation (WSD) is the problem of determining the right sense of a polysemous word in a given context. In this paper, we will investigate the use of unlabeled data for WSD within the framework of semi supervised learning, in which the original labeled dataset is iteratively extended by exploiting unlabeled data. This paper addresses two problems occurring in this approach: deter...

Because of its efficiency, word embedding has been widely used in many natural language processing and text modeling tasks. It aims to represent each by a vector so such that the geometry between these vectors can capture semantic correlations words. An ambiguous often have diverse meanings different contexts, quality which is called polysemy. The bulk studies aimed generate only one single for...

Word Sense Disambiguation (WSD) is the task of computational assignment of correct sense of a polysemous word in a given context. This paper compares three WSD algorithms for Hindi WSD based on corpus statistics. The first algorithm, called corpus-based Lesk, uses sense definitions and a sense tagged training corpus to learn weights of Content Words (CWs). These weights are used in the disambig...

This paper describes two experiments on polysemy judgement and sense annotation. The first experiment enabled us to select the most polysemous words which were used in the second experiment, and which serve as test words for the evaluation of WSD systems. We show that this selection method yields results different from selecting words on the basis of their number of senses in a dictionary, and ...

1. Objective Word Sense Disambiguation (WSD) is the task of determining the right sense of a polysemous word in a given context. This study aims to enhance the performance of supervised-based word sense determination by focusing on feature selection and using bootstrapping techniques. Senses determination of a word is essentially based on the information extracted from the context in which this...

Introduction. The article describes the semantics of verbs mental activity in Mansi and Khanty languages. relevance topic is determined by lack research devoted to analysis semantic classification verbal lexico-semantic groups, primarily comparative terms. purpose work reveal internal organization cognition understanding “to know”, understand”, learn”, notice” Ob-Ugric languages characterize re...

Lexical (and structural) ambiguities make language as expressive as it is. Computational lexicons thus have to cope with a large amount of polysemous words. Research in the last decade (e. g., Pustejovsky (1995), Kilgarriff and Gazdar (1995)) has aimed at identifying different types of polysemy in order to capture underlying regularities. This paper deals with a subtype of regular polysemy illu...