Verb Sense Disambiguation Using Support Vector Machines: Impact of WordNet-Extracted Features

Buscaldi, Davide; Rosso, Paolo; Pla, Ferrán; Segarra, Encarna; Arnal, Emilio Sanchis

doi:10.1007/11671299_21

Cited by 5 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a leaf node is reached, the sense of the word is represented (if possible). Support Vector Machine based algorithms [8] is another supervised approach used for mainly classification algorithm. The goal of this approach is to separate positive examples from negative examples with maximum margin and margin is the distance of hyperplane to the nearest of the positive and negative examples In order to apply the SVM to the WSD task, each nominal feature with possible values was converted into binary (0 or 1) features.…”

Section: Supervised Wsdmentioning

confidence: 99%

A brief review on Word Sense Disambiguation Approaches

Kale¹,

Gawande²

2018

IJET

View full text Add to dashboard Cite

To deal with the problem of text retrieval, machine translation, query processing, speech processing, the word sense disambiguation (WSD) performs very important role. WSD is an AI complete problem. This paper presents the significance, approaches along with work done in the field of WSD. Apple amount of work has been done in WSD for foreign languages but for Indian languages this issue is still about to concern. This paper tries to find out different limitations and challenges in WSD, based on which better approach will be decided by the researchers to solve the problem of WSD.

show abstract

Section: Supervised Wsdmentioning

confidence: 99%

A brief review on Word Sense Disambiguation Approaches

Kale¹,

Gawande²

2018

IJET

View full text Add to dashboard Cite

show abstract

“…Navigli [11] presents a knowledge-rich approach to computing semantic relatedness using a multilingual lexical knowledge base; Cabezas [12] adopts a supervised word sense tagger using SVM; Though these systems have made good effects, compared English with Chinese, both construction and grammatical are different and they can't use SVM to solve Chinese word sense disambiguation. Buscaldi [13] focuses on the disambiguation of verbs by means of SVM and the use of WordNet-extracted features, based on the hyperonyms of context nouns, but it takes all kinds of features as equally important without considering the influence of feature weights.…”

Section: Introductionmentioning

confidence: 99%

Research on Feature Weights of Liheci Word Sense Disambiguation

Zhang

Tian

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

View full text Add to dashboard Cite

Concerning the problems of the comparative translation is not accurate in machine translation and the useful information is unable to match in information retrieval, a liheci word sense disambiguation method is adopted and a classifier model is established using Support Vector Machines(SVM). To improve the accuracy of the liheci word sense disambiguation, it extracts not only local words(LW), local part-of-speeches(POS), local words and part-of-speeches (LWP) but also the middle insert part of the separated form as disambiguation features according to the characteristics of liheci; When the text features are converted to feature vectors for improving on boolean weight method, we can fix feature weights of some type in turn and change the other two types' to verify the disambiguation effect of three kinds of features, respectively. The results show that the effect of LW, LWP is higher than POS. Setting higher feature weights to LW and LWP, the disambiguation accuracy can effectively improve.

show abstract

A novel approach to word sense disambiguation in Bengali language using supervised methodology

et al. 2019

View full text Add to dashboard Cite

An attempt is made in this paper to report how a supervised methodology has been adopted for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications. At the initial stage, four commonly used supervised methods, Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naïve Bayes (NB), are developed at the baseline. These algorithms are applied individually on a data set of 13 most frequently used Bengali ambiguous words. On experimental basis, the baseline strategy is modified with two extensions: (a) inclusion of lemmatization process into the system and (b) bootstrapping of the operational process. As a result, the levels of accuracy of the baseline methods are slightly improved, which is a positive signal for the whole process of disambiguation as it opens scope for further modification of the existing method for better result. In this experiment, the data sets are prepared from the Bengali corpus, developed in the Technology Development for Indian Languages (TDIL) project of the Government of India and from the Bengali WordNet, which is developed at the Indian Statistical Institute, Kolkata. The paper reports the challenges and pitfalls of the work that have been closely observed during the experiment.

show abstract

Verb Sense Disambiguation Using Support Vector Machines: Impact of WordNet-Extracted Features

Cited by 5 publications

References 4 publications

A brief review on Word Sense Disambiguation Approaches

A brief review on Word Sense Disambiguation Approaches

Research on Feature Weights of Liheci Word Sense Disambiguation

A novel approach to word sense disambiguation in Bengali language using supervised methodology

Contact Info

Product

Resources

About