Ontology Matching using BabelNet Dictionary and Word Sense Disambiguation Algorithms

2017 ◽  
Vol 5 (1) ◽  
pp. 196 ◽  
Author(s):  
Mohamed Biniz ◽  
Rachid El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Similarity Measures ◽  
Ontology Matching ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Lesk Algorithm ◽  
Reference Ontology ◽  
Selection Of

<p>Ontology matching is a discipline that means two things: first, the process of discovering correspondences between two different ontologies, and second is the result of this process, that is to say the expression of correspondences. This discipline is a crucial task to solve problems merging and evolving of heterogeneous ontologies in applications of the Semantic Web. This domain imposes several challenges, among them, the selection of appropriate similarity measures to discover the correspondences. In this article, we are interested to study algorithms that calculate the semantic similarity by using Adapted Lesk algorithm, Wu &amp; Palmer Algorithm, Resnik Algorithm, Leacock and Chodorow Algorithm, and similarity flooding between two ontologies and BabelNet as reference ontology, we implement them, and compared experimentally. Overall, the most effective methods are Wu &amp; Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).</p>

Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of BUN in Taiwan Hakka

2021 ◽  
pp. 2050011
Author(s):  
Huei-Ling Lai ◽  
Hsiao-Ling Hsu ◽  
Jyi-Shane Liu ◽  
Chia-Hung Lin ◽  
Yanhong Chen
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Network Models ◽  
Word Sense ◽  
Neural Network Models ◽  
Low Resource ◽  
Sense Disambiguation

While word sense disambiguation (WSD) has been extensively studied in natural language processing, such a task in low-resource languages still receives little attention. Findings based on a few dominant languages may lead to narrow applications. A language-specific WSD system is in need to implement in low-resource languages, for instance, in Taiwan Hakka. This study examines the performance of DNN and Bi-LSTM in WSD tasks on polysemous BUNin Taiwan Hakka. Both models are trained and tested on a small amount of hand-crafted labeled data. Two experiments are designed with four kinds of input features and two window spans to explore what information is needed for the models to achieve their best performance. The results show that to achieve the best performance, DNN and Bi-LSTM models prefer different kinds of input features and window spans.

scholarly journals Word Sense Disambiguation for Chinese Based on Semantics Calculation

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuntong Liu ◽  
Hua Sun
Keyword(s):  
Natural Language Processing ◽  
Computational Complexity ◽  
Natural Language ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Semantic Model ◽  
Sense Disambiguation

In order to use semantics more effectively in natural language processing, a word sense disambiguation method for Chinese based on semantics calculation was proposed. The word sense disambiguation for a Chinese clause could be achieved by solving the semantic model of the natural language; each step of the word sense disambiguation process was discussed in detail; and the computational complexity of the word sense disambiguation process was analyzed. Finally, some experiments were finished to verify the effectiveness of the method.

A Multi-Strategy Word Sense Disambiguation Method

2012 ◽  
Vol 182-183 ◽  
pp. 2109-2112
Author(s):  
Lin Lin Yu ◽  
Deng Feng Xu ◽  
Li Fang Song ◽  
Guo Jie Li ◽  
Xu Dong Song
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Large Area ◽  
Research Areas ◽  
Sense Disambiguation ◽  
Difficult Issue

Word sense disambiguation (WSD) is a critical and difficult issue in natural language processing(NLP), as well as WSD is great significance in large area of research areas of NLP. This paper presents a method of multi-word sense disambiguation strategy. The method combines the method based on match word corpus and the method based on the similarity and relevance very well. While the calculation of similarity and relevance are make full use of the sememe-tree information from HowNet. The experiments show that the proposed WSD method can obtain better results.

scholarly journals Performance Analysis of Sense Embeddings in Multilingual WSD Framework

2021 ◽  
pp. 140-148
Author(s):  
Mr. Prashant Y. Itankar ◽  
Dr. Nikhat Raza
Keyword(s):  
Natural Language Processing ◽  
Performance Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Specific Context ◽  
Sense Disambiguation

Execution of Word Sense Disambiguation (WSD) is one of the difficult undertakings in the space of Natural language processing (NLP). Age of sense clarified corpus for multilingual WSD is far off for most languages regardless of whether assets are accessible. In this paper we propose a solo technique utilizing word and sense embeddings for working on the presentation of WSD frameworks utilizing untagged corpora and make two bags to be specific context bag and wiki sense bag to create the faculties with most noteworthy closeness. Wiki sense bag gives outer information to the framework needed to help the disambiguation exactness. We investigate Word2Vec model to produce the sense back and notice huge execution acquire for our dataset.

scholarly journals Feature Selection

2019 ◽  
Author(s):  
Qasem Al-Tashi
Keyword(s):  
Feature Selection ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Processing Step ◽  
Sense Disambiguation

In the process of natural language, a lot of words have different connotations. The correct sense of a word depends upon the context in which the word occurs. Word sense disambiguation known as the process of selecting the most correct sense of the word in a given sentence. Furthermore, Most of natural language processing applications, such as the extraction of information, machine translation, and Analysing of content are supported by the process of word sense disambiguation which can be an essential pre-processing step for them.

scholarly journals Ambiguity Resolution : An Analytical Study

2020 ◽  
pp. 471-479
Author(s):  
Prashant Y. Itankar ◽  
Nikhat Raza
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Translation ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Lexical Resources ◽  
Human Machine Interaction ◽  
Depth Analysis ◽  
Sense Disambiguation

Natural language processing (NLP) is very much needed in today’s world to enhance human-machine interaction. It is an important concern to process textual data and obtain useful and meaningful information from these texts. NLP parses the texts and provides information to machine for further processing. The present status of NLP’s computational process of identifying the meaning (sense) of a word in a particular context is ambiguous, where the meaning of word in the context is not clear and may point to multiple senses. Ambiguity in understanding correct meaning of texts is hampering the growth and development in various fields of Natural language processing applications like Machine translation, Human Machine interface etc. The process of finding the correct meaning of the ambiguous texts in the given context is called as word sense disambiguation (WSD). WSD is perceived as one of the most challenging problem in the Natural language processing community and is still unsolved. It is evident that different ambiguities exist in natural languages and researchers are contributing to resolve the problem in different languages for successful disambiguation. These ambiguities must be resolved in order to understand the meaning of the text and help to boost NLP processing and applications. Objective is to investigate how WSD can be used to alleviate ambiguities, automatically determine the correct meaning of the ambiguous text and help to boost NLP processing and applications. Resolving ambiguity for translation involves working with various natural language processing techniques to investigate the structure of the languages, availability of lexical resources etc. Word Sense Disambiguation (WSD) in the field of computing linguistics is an area which is still unsolved. This paper focus on the in-depth analysis of such ambiguity, issues in Language Translation, how WSD resolves the ambiguity and contribute towards building a framework.

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