An Evidential Reasoning Approach to Weighted Combination of Classifiers for Word Sense Disambiguation

نویسندگان

  • Cuong Anh Le
  • Van-Nam Huynh
  • Akira Shimazu
چکیده

Arguing that various ways of using context in word sense disambiguation (WSD) can be considered as distinct representations of a polysemous word, a theoretical framework for the weighted combination of soft decisions generated by experts employing these distinct representations is proposed in this paper. Essentially, this approach is based on the Dempster-Shafer theory of evidence. By taking the confidence of individual classifiers into account, a general rule of weighted combination for classifiers is formulated, and then two particular combination schemes are derived. These proposed strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Adaptively entropy-based weighting classifiers in combination using Dempster-Shafer theory for word sense disambiguation

In this paper we introduce an evidential reasoning based framework for weighted combination of classifiers for word sense disambiguation (WSD). Within this framework, we propose a new way of defining adaptively weights of individual classifiers based on ambiguity measures associated with their decisions with respect to each particular pattern under classification, where the ambiguity measure is...

متن کامل

Combining Heterogeneous Classifiers for Word Sense Disambiguation

This paper discusses ensembles of simple but heterogeneous classifiers for word-sense disambiguation, examining the Stanford-CS224N system entered in the SENSEVAL-2 English lexical sample task. First-order classifiers are combined by a second-order classifier, which variously uses majority voting, weighted voting, or a maximum entropy model. While individual first-order classifiers perform comp...

متن کامل

Combining classifiers for word sense disambiguation based on Dempster-Shafer theory and OWA operators

In this paper, we discuss a framework for weighted combination of classifiers for word sense disambiguation (WSD). This framework is essentially based on DempsterShafer theory of evidence (Dempster, 1967; Shafer, 1976) and ordered weighted averaging (OWA) operators (Yager, 1988). We first determine various kinds of features which could provide complementarily linguistic information for the cont...

متن کامل

Combining Classifiers for word sense disambiguation

Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Näıve Bayes, Cosine, Decision List, Transformation-based Learning and...

متن کامل

Trajectory Based Word Sense Disambiguation

Classifier combination is a promising way to improve performance of word sense disambiguation. We propose a new combinational method in this paper. We first construct a series of Naïve Bayesian classifiers along a sequence of orderly varying sized windows of context, and perform sense selection for both training samples and test samples using these classifiers. We thus get a sense selection tra...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005