scholarly journals Sense disambiguation in polysemous words: cognitive perspective

2013 ◽  
Vol 6 (3) ◽  
pp. 60
Author(s):  
Vera I. Zabotkina ◽  
Elena L. Boyarskaya
Keyword(s):  
Cognitive Perspective ◽  
Sense Disambiguation ◽  
Polysemous Words

scholarly journals Techniques for Lexical Semantics in Hindi Language

2019 ◽  
Vol 8 (12) ◽  
pp. 4075-4080
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Official Language ◽  
Indian Languages ◽  
Semantic Task ◽  
Sense Disambiguation ◽  
Polysemous Word ◽  
The Given ◽  
Polysemous Words

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is constrained by non-availability of the standard corpora. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the available sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favourable results are achieved

scholarly journals EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings

2020 ◽  
Author(s):  
Agostina Calabrese ◽  
Michele Bevilacqua ◽  
Roberto Navigli
Keyword(s):  
Word Sense Disambiguation ◽  
Learning Task ◽  
Concept Image ◽  
Word Sense ◽  
Alternative Technologies ◽  
Open Set ◽  
Sense Disambiguation ◽  
Image Pairs ◽  
Polysemous Words ◽  
Limited Coverage

The problem of grounding language in vision is increasingly attracting scholarly efforts. As of now, however, most of the approaches have been limited to word embeddings, which are not capable of handling polysemous words. This is mainly due to the limited coverage of the available semantically-annotated datasets, hence forcing research to rely on alternative technologies (i.e., image search engines). To address this issue, we introduce EViLBERT, an approach which is able to perform image classification over an open set of concepts, both concrete and non-concrete. Our approach is based on the recently introduced Vision-Language Pretraining (VLP) model, and builds upon a manually-annotated dataset of concept-image pairs. We use our technique to clean up the image-to-concept mapping that is provided within a multilingual knowledge base, resulting in over 258,000 images associated with 42,500 concepts. We show that our VLP-based model can be used to create multimodal sense embeddings starting from our automatically-created dataset. In turn, we also show that these multimodal embeddings improve the performance of a Word Sense Disambiguation architecture over a strong unimodal baseline. We release code, dataset and embeddings at http://babelpic.org.

scholarly journals A Knowledge Based Word Sense Disambiguation in Telugu Language

2020 ◽  
Vol 10 (1) ◽  
pp. 440-445
Keyword(s):  
Computational Linguistics ◽  
Word Sense Disambiguation ◽  
The Other ◽  
Word Sense ◽  
Knowledge Based ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
The Senses ◽  
Definition Of ◽  
Polysemous Words

Telugu (తెలుగు) is one of the Dravidian languages which are morphologically rich. As within the other languages, it too consists of ambiguous words/phrases which have one-of-a-kind meanings in special contexts. Such words are referred as polysemous words i.e. words having a couple of experiences. A Knowledge based approach is proposed for disambiguating Telugu polysemous phrases using the computational linguistics tool, IndoWordNet. The task of WSD (Word sense disambiguation) requires finding out the similarity among the target phrase and the nearby phrase. In this approach, the similarity is calculated either by means of locating out the range of similar phrases (intersection) between the glosses (definition) of the target and nearby words or by way of finding out the exact occurrence of the nearby phrase's sense in the hierarchy (hypernyms/hyponyms) of the target phrase's senses. The above parameters are changed by using the intersection use of not simplest the glosses but also by using which include the related words. Additionally, it is a third parameter 'distance' which measures the distance among the target and nearby phrases. The proposed method makes use of greater parameters for calculating similarity. It scores the senses based on the general impact of parameters i.e. intersection, hierarchy and distance, after which chooses the sense with the best score. The correct meaning of Telugu polysemous phrase could be identified with this technique.

Word sense disambiguation using implicit information

2019 ◽  
Vol 26 (4) ◽  
pp. 413-432 ◽  
Author(s):  
Goonjan Jain ◽  
D.K. Lobiyal
Keyword(s):  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Implicit Information ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Novel Method ◽  
Unsupervised Approach ◽  
Polysemous Words ◽  
Better Than

AbstractHumans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous word. The experiments show that the proposed algorithm interprets the correct sense for both homonyms and polysemous words. Our proposed algorithm has performed better than the approaches presented in the SemEval-2013 task for word sense disambiguation and has shown an accuracy of 79.6 percent, which is 2.5 percent better than the best unsupervised approach in SemEval-2007.

scholarly journals All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

2020 ◽  
Vol 20 (4) ◽  
pp. 90-107
Author(s):  
Bolshina Angelina ◽  
Natalia Loukachevitch
Keyword(s):  
Word Sense Disambiguation ◽  
Automatic Generation ◽  
Word Sense ◽  
High Quality ◽  
Full Coverage ◽  
Sense Disambiguation ◽  
Polysemous Words

AbstractThe limited amount of the sense annotated data is a big challenge for the word sense disambiguation task. As a solution to this problem, we propose an algorithm of automatic generation and labelling of the training collections based on the monosemous relatives concept. In this article we explore the limits of this algorithm: we employ it to harvest training collections for all ambiguous nouns, verbs and adjectives presented in RuWordNet thesaurus and then evaluate the quality of the obtained collections. We demonstrate that our approach can create high-quality labelled collections with almost full-coverage of the RuWordNet polysemous words. Furthermore, we show that our method can be applied to the Word-in-Context task.

scholarly journals Global Word Sense Disambiguation of Polysemous Words in Telugu Language

2020 ◽  
Vol 10 (1) ◽  
pp. 420-425
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Text Summarization ◽  
Word Sense ◽  
Significant Issue ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Sequencing Technique ◽  
Polysemous Words ◽  
Word Senses

Word Sense Disambiguation (WSD) is a significant issue in Natural Language Processing (NLP). WSD refers to the capacity of recognizing the correct sense of a word in a given context. It can improve numerous NLP applications such as machine translation, text summarization, information retrieval, or sentiment analysis. This paper proposes an approach named ShotgunWSD. Shotgun WSD is an unsupervised and knowledgebased algorithm for global word sense disambiguation. The algorithm is motivated by the Shotgun sequencing technique. Shotgun WSD is proposed to disambiguate the word senses of Telugu document with three functional phases. The Shotgun WSD achieves the better performance than other approaches of WSD in the disambiguating sense of ambiguous words in Telugu documents. The dataset is used in the Indo-WordNet.

A cognitive perspective on parent-young recognition in kittiwakes (Rissa tridactyla)

1991 ◽  
Author(s):  
Rita E. Anderson ◽  
Julie M. Porter ◽  
Anne E. Storey
Keyword(s):  
Cognitive Perspective ◽  
Rissa Tridactyla

The modern philosophy of history: historical-anthropological problematics

2018 ◽  
pp. 4-9
Author(s):  
Sp. Sh. Aytov
Keyword(s):  
Modern Philosophy ◽  
Philosophy Of History ◽  
Cognitive Perspective

This article is devoted to the analysis of the formation of the cognitive perspective of the historical-anthropological dimension of modern philosophy of history. The influence of the mentioned problem field on the development of intellectual directions of modern philosophical and historical studios was studied.

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