scholarly journals A Learning-Based Approach for Biomedical Word Sense Disambiguation

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Hisham Al-Mubaid ◽  
Sandeep Gungu
Keyword(s):  
Research Effort ◽  
Word Sense Disambiguation ◽  
Interaction Model ◽  
Biomedical Domain ◽  
Word Sense ◽  
Text Annotation ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Supervised Methods ◽  
Automatic Text

In the biomedical domain, word sense ambiguity is a widely spread problem with bioinformatics research effort devoted to it being not commensurate and allowing for more development. This paper presents and evaluates a learning-based approach for sense disambiguation within the biomedical domain. The main limitation with supervised methods is the need for a corpus of manually disambiguated instances of the ambiguous words. However, the advances in automatic text annotation and tagging techniques with the help of the plethora of knowledge sources like ontologies and text literature in the biomedical domain will help lessen this limitation. The proposed method utilizes the interaction model (mutual information) between the context words and the senses of the target word to induce reliable learning models for sense disambiguation. The method has been evaluated with the benchmark dataset NLM-WSD with various settings and in biomedical entity species disambiguation. The evaluation results showed that the approach is very competitive and outperforms recently reported results of other published techniques.

Word Sense Disambiguation in the Biomedical Domain: An Overview

2005 ◽  
Vol 12 (5) ◽  
pp. 554-565 ◽  
Author(s):  
Martijn J. Schuemie ◽  
Jan A. Kors ◽  
Barend Mons
Keyword(s):  
Word Sense Disambiguation ◽  
Biomedical Domain ◽  
Word Sense ◽  
Sense Disambiguation

A HIGHLY ACCURATE BOOTSTRAPPING ALGORITHM FOR WORD SENSE DISAMBIGUATION

2001 ◽  
Vol 10 (01n02) ◽  
pp. 5-21 ◽  
Author(s):  
RADA F. MIHALCEA ◽  
DAN I. MOLDOVAN
Keyword(s):  
High Precision ◽  
Word Sense Disambiguation ◽  
Original Text ◽  
Word Sense ◽  
New Words ◽  
Input Text ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Very High

In this paper, we present a bootstrapping algorithm for Word Sense Disambiguation which succeeds in disambiguating a subset of the words in the input text with very high precision. It uses WordNet and a semantic tagged corpus, for the purpose of identifying the correct sense of the words in a given text. The bootstrapping process initializes a set of ambiguous words with all the nouns and verbs in the text. It then applies various disambiguation procedures and builds a set of disambiguated words: new words are sense tagged based on their relation to the already disambiguated words, and then added to the set. This process allows us to identify, in the original text, a set of words which can be disambiguated with high precision; 55% of the verbs and nouns are disambiguated with an accuracy of 92%.

scholarly journals Co-occurrence graphs for word sense disambiguation in the biomedical domain

2018 ◽  
Vol 87 ◽  
pp. 9-19 ◽  
Author(s):  
Andres Duque ◽  
Mark Stevenson ◽  
Juan Martinez-Romo ◽  
Lourdes Araujo
Keyword(s):  
Word Sense Disambiguation ◽  
Biomedical Domain ◽  
Word Sense ◽  
Sense Disambiguation

A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

2013 ◽  
pp. 1306-1316
Author(s):  
Wei Xiong ◽  
Min Song ◽  
Lori deVersterre
Keyword(s):  
Support Vector Machines ◽  
Search Engine ◽  
Word Sense Disambiguation ◽  
Support Vector ◽  
Biomedical Domain ◽  
Word Sense ◽  
Accuracy Measure ◽  
Vector Machines ◽  
Sense Disambiguation ◽  
Visualization Application

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL’s clustering technique.

scholarly journals Generating Sense Inventories for Ambiguous Arabic Words

2021 ◽  
Author(s):  
Marwah Alian ◽  
Arafat Awajan
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Arabic Word ◽  
Word Sense Induction ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Sentence Similarity ◽  
Unsupervised Approach ◽  
Similar Accuracy

The process of selecting the appropriate meaning of an ambigous word according to its context is known as word sense disambiguation. In this research, we generate a number of Arabic sense inventories based on an unsupervised approach and different pre-trained embeddings, such as Aravec, Fast text, and Arabic-News embeddings. The resulted inventories from the pre-trained embeddings are evaluated to investigate their efficiency in Arabic word sense disambiguation and sentence similarity. The sense inventories are generated using an unsupervised approach that is based on a graph-based word sense induction algorithm. Results show that the Aravec-Twitter inventory achieves the best accuracy of 0.47 for 50 neighbors and a close accuracy to the Fast text inventory for 200 neighbors while it provides similar accuracy to the Arabic-News inventory for 100neighbors. The experiment of replacing ambiguous words with their sense vectors is tested for sentence similarity using all sense inventories and the results show that using Aravec-Twitter sense inventory provides a better correlation value

scholarly journals Analysis of the methods of word sense disambiguation in the biomedical domain

Doklady BGUIR ◽  
2019 ◽  
pp. 60-65
Author(s):  
A. V. Pashuk ◽  
A. B. Gurinovich ◽  
N. A. Volorova ◽  
A. P. Kuznetsov
Keyword(s):  
Word Sense Disambiguation ◽  
Biomedical Domain ◽  
Word Sense ◽  
Sense Disambiguation

A Word Sense Disambiguation Method Based on Reconstruction of Context by Correlation

2011 ◽  
Vol 135-136 ◽  
pp. 160-166 ◽  
Author(s):  
Xin Hua Fan ◽  
Bing Jun Zhang ◽  
Dong Zhou
Keyword(s):  
Information Entropy ◽  
Average Distance ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Experimental Results ◽  
Occurrence Frequency ◽  
Word Sense ◽  
Occurrence Data ◽  
Ambiguous Words ◽  
Sense Disambiguation

This paper presents a word sense disambiguation method by reconstructing the context using the correlation between words. Firstly, we figure out the relevance between words though the statistical quantity(co-occurrence frequency , the average distance and the information entropy) from the corpus. Secondly, we see the words that have lager correlation value between ambiguous word than other words in the context as the important words, and use this kind of words to reconstruct the context, then we use the reconstructed context as the new context of the ambiguous words .In the end, we use the method of the sememe co-occurrence data[10] for word sense disambiguation. The experimental results have proved the feasibility of this method.

scholarly journals WSD-GAN: Word Sense Disambiguation Using Generative Adversarial Networks

2019 ◽  
Vol 33 ◽  
pp. 9943-9944 ◽  
Author(s):  
Zijian Hu ◽  
Fuli Luo ◽  
Yutong Tan ◽  
Wenxin Zeng ◽  
Zhifang Sui
Keyword(s):  
Probability Distribution ◽  
Word Sense Disambiguation ◽  
Generative Adversarial Networks ◽  
Word Sense ◽  
Model Based ◽  
Knowledge Based ◽  
Sense Disambiguation ◽  
Supervised Methods ◽  
The Right ◽  
Word Senses

Word Sense Disambiguation (WSD), as a tough task in Natural Language Processing (NLP), aims to identify the correct sense of an ambiguous word in a given context. There are two mainstreams in WSD. Supervised methods mainly utilize labeled context to train a classifier which generates the right probability distribution of word senses. Meanwhile knowledge-based (unsupervised) methods which focus on glosses (word sense definitions) always calculate the similarity of context-gloss pair as score to find out the right word sense. In this paper, we propose a generative adversarial framework WSD-GAN which combines two mainstream methods in WSD. The generative model, based on supervised methods, tries to generate a probability distribution over the word senses. Meanwhile the discriminative model, based on knowledge-based methods, focuses on predicting the relevancy of the context-gloss pairs and identifies the correct pairs over the others. Furthermore, in order to optimize both two models, we leverage policy gradient to enhance the performances of the two models mutually. Our experimental results show that WSD-GAN achieves competitive results on several English all-words WSD datasets.

A Comparative Study of an Unsupervised Word Sense Disambiguation Approach

2012 ◽  
pp. 414-424
Author(s):  
Wei Xiong ◽  
Min Song ◽  
Lori deVersterre
Keyword(s):  
Support Vector Machines ◽  
Search Engine ◽  
Word Sense Disambiguation ◽  
Support Vector ◽  
Biomedical Domain ◽  
Word Sense ◽  
Accuracy Measure ◽  
Vector Machines ◽  
Sense Disambiguation ◽  
Visualization Application

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL’s clustering technique.

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