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.

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.

A Semantic Scattering model for the automatic interpretation of English genitives

2009 ◽  
Vol 15 (2) ◽  
pp. 215-239 ◽  
Author(s):  
ADRIANA BADULESCU ◽  
DAN MOLDOVAN
Keyword(s):  
Support Vector Machines ◽  
Decision Trees ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Naïve Bayes ◽  
Semantic Relations ◽  
Support Vector ◽  
Word Sense ◽  
Vector Machines ◽  
Bayes Algorithm

AbstractAn important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper addresses the automatic classification of thesemantic relationsexpressed by English genitives. A learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations in a corpus. The semantic and contextual features of the genitive's noun phrase constituents play a key role in the identification of the semantic relation. The algorithm was trained and tested on a corpus of approximately 20,000 sentences and achieved an f-measure of 79.80 per cent for of-genitives, far better than the 40.60 per cent obtained using a Decision Trees algorithm, the 50.55 per cent obtained using a Naive Bayes algorithm, or the 72.13 per cent obtained using a Support Vector Machines algorithm on the same corpus using the same features. The results were similar for s-genitives: 78.45 per cent using Semantic Scattering, 47.00 per cent using Decision Trees, 43.70 per cent using Naive Bayes, and 70.32 per cent using a Support Vector Machines algorithm. The results demonstrate the importance of word sense disambiguation and semantic generalization/specialization for this task. They also demonstrate that different patterns (in our case the two types of genitive constructions) encode different semantic information and should be treated differently in the sense that different models should be built for different patterns.

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

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

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

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.

Using Exponential Kernel for Semi-Supervised Word Sense Disambiguation

2016 ◽  
Vol 13 (10) ◽  
pp. 6929-6934
Author(s):  
Junting Chen ◽  
Liyun Zhong ◽  
Caiyun Cai
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Word Sense ◽  
Exponential Kernel ◽  
Sense Disambiguation

Word sense disambiguation (WSD) in natural language text is a fundamental semantic understanding task at the lexical level in natural language processing (NLP) applications. Kernel methods such as support vector machine (SVM) have been successfully applied to WSD. This is mainly due to their relatively high classification accuracy as well as their ability to handle high dimensional and sparse data. A significant challenge in WSD is to reduce the need for labeled training data while maintaining an acceptable performance. In this paper, we present a semi-supervised technique using the exponential kernel for WSD. Specifically, the semantic similarities between terms are first determined with both labeled and unlabeled training data by means of a diffusion process on a graph defined by lexicon and co-occurrence information, and the exponential kernel is then constructed based on the learned semantic similarity. Finally, the SVM classifier trains a model for each class during the training phase and this model is then applied to all test examples in the test phase. The main feature of this approach is that it takes advantage of the exponential kernel to reveal the semantic similarities between terms in an unsupervised manner, which provides a kernel framework for semi-supervised learning. Experiments on several SENSEVAL benchmark data sets demonstrate the proposed approach is sound and effective.

WEB INFORMATION ACQUISITION BY PERSONAL SEARCH ENGINE BASED ON SVM

2005 ◽  
Vol 02 (04) ◽  
pp. 345-352 ◽  
Author(s):  
RUJING WANG ◽  
DEJI WANG
Keyword(s):  
Support Vector Machines ◽  
Semantic Similarity ◽  
Search Engine ◽  
Information Acquisition ◽  
Domain Knowledge ◽  
Support Vector ◽  
Similar Measurement ◽  
Personal Preference ◽  
Web Information ◽  
Vector Machines

The quantity of web information is growing exponentially with time, and the challenge of acquiring information efficiently by personal search engine is increasingly complex. Personal preference is not easily described but can be observed from the examples given. Supervised Clustering with Support Vector Machines have been introduced to learn personal preference, however, it is not specialized and cannot be applied to the domain knowledge. In this paper, we introduce the ontology and semantic similarity into SVM as similar measurement. Experiments on agricultural information acquisition validate that it is highly effective.

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