Abstract Text and Knowledge Bases are complementary sources of information. Given the
success of distributed word representations learned from text, several techniques to infuse …

The state-of-the-art multi-task multi-view learning (MTMV) tackles the learning scenario
where multiple tasks are associated with each other via multiple shared feature views …

Named entity disambiguation is the task of linking entity mentions to their intended referent,
as represented in a Knowledge Base, usually derived from Wikipedia. In this paper, we …

Graph processing participates a vital role in mining relational data. However, the intensive
but inefficient memory accesses make graph processing applications severely bottlenecked …

This paper presents our submissions for semantic textual similarity task in SemEval 2016.
Based on several traditional features (ie, string-based, corpus-based, machine translation …

In this paper we present an approach for the enrichment of WSD knowledge bases with data-
driven relations from a gold standard corpus (annotated with word senses, valency …

Abstract Named Entity Disambiguation (NED) algorithms disambiguate mentions of named
entities with respect to a knowledge-base, but sometimes the context might be poor or …

This work presents parallel corpora automatically annotated with several NLP tools,
including lemma and part-of-speech tagging, named-entity recognition and classification …

In this paper we present an analysis of different semantic relations extracted from WordNet,
Extended WordNet and SemCor, with respect to their role in the task of knowledge-based …

In this paper, we present a comparative study between some well-known knowledge
structures, applied in the domain of textual query disambiguation, which are Wikipedia …