scholarly journals An Associative Concept Dictionary for Natural Language Processing: Text Summarization and Word Sense Disambiguation

2011 ◽  
Vol 12 (3) ◽  
pp. 261-278
Author(s):  
Jun Okamoto ◽  
Shun Ishizaki
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Text Summarization ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals Networks and Natural Language Processing

AI Magazine ◽  
2008 ◽  
Vol 29 (3) ◽  
pp. 16 ◽  
Author(s):  
Dragomir R. Radev ◽  
Rada Mihalcea
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Text Clustering ◽  
Text Summarization ◽  
Syntactic Parsing ◽  
Word Sense ◽  
Ontology Construction ◽  
Sense Disambiguation

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

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.

scholarly journals Supervised Word Sense Disambiguation using Decision Tree

2019 ◽  
Vol 8 (2) ◽  
pp. 4043-4047
Keyword(s):  
Natural Language Processing ◽  
Decision Tree ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
English Sentence ◽  
Sense Disambiguation ◽  
Almost All

Semantic processing is an essential task in natural language processing. In semantic processing it has observed that some words have more than one meaning. Multiple meanings of a word create serious problems to linguists which produces ambiguity in sentence. Word Sense Disambiguation is one of the main challenges in natural language processing which is present in almost all the languages. By existing knowledge and experience human can certainly disambiguate the words but for machine it is difficult task. In the proposed work, we are resolving the ambiguity of all open class word in English sentence and translating it to the Hindi sentence. We have used decision tree as a classifier. For improving the speed of translation we have used the concept of translation memory.

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