Abstract As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most approaches to ATE broadly follow the traditional hybrid methodology, by first extracting list of unique candidate terms, and classifying these candidates based on predicted probability that they are valid terms. However, rise neural n...

In this paper we present a model for multifaceted tagging, i.e. tagging enriched with contextual information. We present TagMe!, a social tagging front-end for Flickr images, that provides multifaceted tagging functionality: It enables users to attach tag assignments to a specific area within an image and to categorize tag assignments. Moreover, TagMe! maps tags and categories to DBpedia URIs t...

Gene tagging facilitates systematic genomic and proteomic analyses but chromosomal tagging typically disrupts gene regulatory sequences. Here we describe a seamless gene tagging approach that preserves endogenous gene regulation and is potentially applicable in any species with efficient DNA double-strand break repair by homologous recombination. We implement seamless tagging in Saccharomyces c...