Description: This paper discusses integrating knowledge for subjectivity sense labeling. Abstract: This paper introduces an integrative approach to automatic word sense subjectivity annotation. We use features that exploit the hierarchical structure and domain information in lexical resources such as WordNet, as well as other types of features that measure the similarity of glosses and the overlap among sets of semantically related words. Integrated in a machine learning framework, the entire set of features is found to give better results than any individual type of feature.

Description: This paper discusses investigations in unsupervised back-of-the-book indexing. Abstract: This paper describes our experiments with unsupervised methods for back-of-the-book index construction. Through comparative evaluations performed on a gold standard data set of 29 books and their corresponding indexes, the authors draw conclusions as to what are the most accurate unsupervised methods for automatic index construction. We show that if the right sequence of methods and heuristics is used, the performance of an unsupervised back-of-the-book index construction system can be raised with up to 250% relative increase in F-measure as compared to the performance of a system based on the traditional tf*idf weighting scheme.

Description: This paper discusses a language independent algorithm for single and multiple document summarization. Abstract: This paper describes a method for language independent extractive summarization that relies on iterative graph-based ranking algorithms. Through evaluations performed on a single document summarization task for English and Portuguese, we show that the method performs equally well regardless of the language. Moreover, we show how a meta-summarizer relying on a layered application of techniques for single-document summarization can be turned into an effective method for multi-document summarization.

Description: This poster received 1st place in the 2013 Graduate Exhibition in the Engineering category. Abstract: High energy lasers emerged as an innovative and potential industrial tool to fabricate complex shapes on structural ceramics which is otherwise difficult using conventional machining techniques. However, obtaining a desired surface finish at higher material removal rate during laser machining of structural ceramics is still a critical issue. In this situation, the better understanding of various physical phenomena such as heat transfer, fluid flow, recoil pressure, Marangoni convection, and surface tension and its influence on the evolution of typical surface topography during laser machining could be more helpful. In light of this, this study was attempted to present the state of the art of laser machining of alumina using an integrated experimental and computational approach. A multistep computational model based on COMSOL™ Multiphysics was developed to study the effect of various physical phenomena on the generation of surface topography for various laser machining conditions. Furthermore, this process model can be used as a handy tool for the process engineers to configure the process variables (laser power, scanning speed, pulse rate, size of overlap) to obtain the specified quality characteristics. The surface topography of laser machined ...

Description: This paper discusses learning multilingual subjective language via cross-lingual projections. Abstract: This paper explores methods for generating subjectivity analysis resources in a new language by leveraging on the tools and resources available in English. Given a bridge between English and the selected target language (e.g., a bilingual dictionary or a parallel corpus), the methods can be used to rapidly create tools for subjectivity analysis in the new language.