In this paper, we present the results of a project that seeks to transform low-level features to a higher level of meaning. This project concerns a technique, latent semantic indexing (LSI), in conjunction with normalization and term weighting, which have been used for full-text retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords, sometimes, called co...

– In this paper, we present the results of our work that seeks to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords, sometimes, calle...

Since the introduction in the early nineties, feature models receive a great attention in industry and academia. Industrial success stories in applying feature models for modeling software product lines, and using them for configuring software-intensive systems motivate academia to discover ways to integrate different feature dependencies into the feature model, and automate verified feature co...

This paper presents a new method to tackle the feature interaction problem in Internet telephony with the CPL (Call Processing Language) programmable service environment. To cope with the problems of the programmable service, we first propose a notion of semantic warnings, which are guidelines for non-experts to assure semantic correctness of individual CPL scripts. Then, we define feature inte...

Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and automatic emotion lexicon construction. In this work, we explore features for recognizing word emotion. Based on RenCECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, several contextual features and their combination have been experimented. Then PLSA (proba...

Semantic attributes have been proposed to bridge the semantic gap between low-level feature representation and high-level semantic understanding of visual objects. Obtaining a good representation of semantic attributes usually requires learning from high-dimensional low-level features, which not only significantly increases the time and space requirement but also degrades the performance due to...

Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) in the training seen classes or testing unseen classes. However, the projection function cannot be used between the feature space and multi-semantic embedding spaces, which have the diversity characteristic for describing the different sem...

In the present study, the authors examined with event-related brain potentials whether phonological encoding in picture naming is mediated by basic semantic feature retrieval or proceeds independently. In a manual 2-choice go/no-go task the choice response depended on a semantic classification (animal vs. object) and the execution decision was contingent on a classification of name phonology (v...

INTRODUCTION This paper explores the benefits of using n-grams and semantic features for the classification of disease outbreak reports, in the context of the BioCaster disease outbreak report text mining system. A novel feature of this work is the use of a general purpose semantic tagger - the USAS tagger - to generate features. BACKGROUND We outline the application context for this work (th...