Satellite Workshop On Language, Artificial Intelligence and Computer Science for Natural Language Processing Applications (LAICS-NLP): Discovery of Meaning from Text

This paper proposes a novel method to disambiguate important words from a collection of documents. The hypothesis that underlies this approach is that there is a minimal set of senses that are significant in characterizing a context. We extend Yarowsky’s one sense per discourse [13] further to a collection of related documents rather than a single document. We perform distributed clustering on a set of features representing each of the top ten categories of documents in the Reuters-21578 dataset. Groups of terms that have a similar term distributional pattern across documents were identified. WordNet-based similarity measurement was then computed for terms within each cluster. An aggregation of the associations in WordNet that was employed to ascertain term similarity within clusters has provided a means of identifying clusters’ root senses

Automated discovery of concepts from text

The proposed framework combines machine learning and semantic modeling in enabling the discovery process. The framework enables the extraction of concepts and the formulation of a structured organization and labeling of concepts to be used by text processing applications such as text summarization and text categorization

EBario map-based tourism website

This map-based Bario tourism website is part of the eBario research project. It is developed to promote tourism in Bario and to preserve its cultural heritage. The website contains information about Bario and the Kelabits. In addition, users may make accommodation and tourist guide reservation through the website. The website also consists of a zoom-able map that is built using Scalable Vector Graphic (SVG) format