OE: WSD Using Optimal Ensembling (OE) Method
نویسنده
چکیده
Optimal ensembling (OE) is a word sense disambiguation (WSD) method using word-specific training factors (average positive vs negative training per sense, posex and negex) to predict best system (classifier algorithm / applicable feature set) for given target word. Our official entry (OE1) in Senseval-4 Task 17 (coarse-grained English lexical sample task) contained many design flaws and thus failed to show the whole potential of the method, finishing -4.9% behind top system (+0.5 gain over best base system). A fixed system (OE2) finished only -3.4% behind (+2.0% net gain). All our systems were 'closed', i.e. used the official training data only (average 56 training examples per each sense). We also show that the official evaluation measure tends to favor systems that do well with high-trained words.
منابع مشابه
Study of the amount of oleuropein level in the leaves of olive varieties cultivated in Khorramabad (Iran)
Background: Oleuropein (OE) is the most abundant biophenol compounds in olive leaves and its healthy effects as a strong antioxidant is well known. The oleuropein level in olives depends upon cultivated variety, climate, place, time and the method of extraction. In Lorestan province, many varieties of olive have been cultivated that the study of OE level in their leaves, as whilst sources of th...
متن کاملCase-Sensitivity of Classifiers for WSD: Complex Systems Disambiguate Tough Words Better
We present a novel method for improving disambiguation accuracy by building an optimal ensemble (OE) of systems where we predict the best available system for target word using a priori case factors (e.g. amount of training per sense). We report promising results of a series of best-system prediction tests (best prediction accuracy is 0.92) and show that complex/simple systems disambiguate toug...
متن کاملThe Effect of Knowledge Management on Organizational Entrepreneurship among Agricultural Extension Experts in Kermanshah Province, Iran
In today's turbulent business environment, organizations face the need to rapidly respond to demands, explore new opportunities, apply evolving technologies, and create novel competitive advantages. Knowledge Management (KM) and Organizational Entrepreneurship (OE) are two strategic tools through which companies can concurrently improve their competitive advantage while seeking new potential op...
متن کاملDefining Classifier Regions for WSD Ensembles Using Word Space Features
Based on recent evaluation of word sense disambiguation (WSD) systems [10], disambiguation methods have reached a standstill. In [10] we showed that it is possible to predict the best system for target word using word features and that using this 'optimal ensembling method' more accurate WSD ensembles can be built (3-5% over Senseval state of the art systems with the same amount of possible pot...
متن کاملOptimal design of aperiodic, vertical silicon nanowire structures for photovoltaics.
We design a partially aperiodic, vertically-aligned silicon nanowire array that maximizes photovoltaic absorption. The optimal structure is obtained using a random walk algorithm with transfer matrix method based electromagnetic forward solver. The optimal, aperiodic structure exhibits a 2.35 times enhancement in ultimate efficiency compared to its periodic counterpart. The spectral behavior mi...
متن کامل