TL;DR
Melita goes beyond the dichotomy rule learning Vs rule writing of classic annotation systems, as it allows adopting different strategies, from annotating examples in a corpus for training a learner to rule writing and even a mixture of them.
Rather than scaling performance uniformly across the entire evaluation suite, the ontology layer acts as a targeted traceability and semantic refinement filter that contributes information beyond filtered-profile selection alone and produces a metric-dependent change in classifier behaviour at the validation-selected threshold.
This research aims to create an ontology-based method for summarizing government documents by improving understanding using domain-specific terms and their relationships and shows that the summaries are accurate, meaningful and match the reference summaries.
ATEM is a term extraction tool for web and mobile environments that incorporates a hybrid method for identifying relevant terms in English-language scientific literature on IoT and contributes to the development of lexicographic resources; language translation; and the creation of shared databases.
The goal of this work is to put into context recent updates in ontology engineering and present the necessary additional information targeted to reuse by providing an updated overview and proposing a two-tier categorisation, believed to be the first review to offer such an intra-category classification.
