Recognition using only visual evidence cannot always be successful due to limitations of information and resources available during training. Considering relation among lexicon entries is sometimes useful for decision making. In this paper, we present a method to capture lexical similarity of a lexicon and reliability of a character recognizer which serve to capture the dynamism of the environm...

Distributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional (asymmetric) similarity measures that a...

This work describes the evaluations of three different approaches, Lexical Match, Sense Similarity based on Personalized Page Rank, and Semantic Match based on Shallow Frame Structures, for word sense alignment of verbs between two Italian lexical-semantic resources, MultiWordNet and the Senso Comune Lexicon. The results obtained are quite satisfying with a final F1 score of 0.47 when merging t...

This paper describes two distinct attempts at the Senseval2 Japanese translation task. The first implementation is based on lexical similarity and builds on the results of Baldwin (2001b; 2001a), whereas the second is based on structural similarity via the medium of parse trees and includes a basic model of conceptual similarity. Despite its simplistic nature, the lexical method was found to pe...

This paper presents a methodology for interacting with images using both text and visual features. When images are entered into the database, they are associated with a piece of text (typically a web page), which is used to derive a lexical index for the image. At the same time, visual features are computed. The similarity between two images is then a combination between their visual similarity...

Ontology matching is an important task to achieve interoperation between semantic web applications using different ontologies. Structural similarity plays a central role in ontology matching. However, the existing approaches rely heavily on lexical similarity, and they mix up lexical similarity with structural similarity. In this paper, we present a graph matching approach for ontologies, calle...

Many methods are available for computing semantic similarity between individual words, but certain NLP tasks require the comparison of word pairs. This paper presents a kernel-based framework for application to relational reasoning tasks of this kind. The model presented here combines information about two distinct types of word pair similarity: lexical similarity and relational similarity. We ...