Semantic representation is receiving growing attention in NLP in the past few years, and
many proposals for semantic schemes (eg, AMR, UCCA, GMB, UDS) have been put forth …

After entering the big data era, a new term ofbig knowledge'has been coined to deal with
challenges in mining a mass of knowledge from big data. While researchers used to explore …

The goal of the Groningen Meaning Bank (GMB) is to obtain a large corpus of English texts
annotated with formal meaning representations. Since manually annotating a …

We propose a graph-based semantic model for representing document content. Our method
relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring …

Using semantic technologies for mining and intelligent information access to social media is
a challenging, emerging research area. Traditional search methods are no longer able to …

Abstract, structured, representations of knowledge such as lexicons, taxonomies, and
ontologies have proven to be powerful resources not only for the systematization of …

Contributive resources, such as Wikipedia, have proved to be valuable to Natural Language
Processing or multilingual Information Retrieval applications. This work focusses on …

Abstract 'Open Data'has become very important in a wide range of fields. However for
linguistics, much data is still published in proprietary, closed formats and is not made …

Recent years have witnessed a surge in the amount of semantic information published on
the Web. Indeed, the Web of Data, a subset of the Semantic Web, has been increasing …

We consider the task of identifying and labeling the semantic arguments of a predicate that
evokes a FrameNet frame. This task is challenging because there are only a few thousand …