Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. …
Recently, Natural Language Processing (NLP) has witnessed an impressive progress in many areas, due to the advent of novel, pretrained contextual representation models. In particular, Devlin et al. (2019) proposed a model, called BERT (Bidirectional …
In this paper we deal with complex attributed graphs which can exhibit rich connectivity patterns and whose nodes are often associated with attributes, such as text or images. In order to analyze these graphs, the primary challenge is to find an …
The huge amount of textual user-generated content on the Web has incredibly grown in the last decade, creating new relevant opportunities for different real-world applications and domains. In particular, microblogging platforms enables the collection …
Knowledge Graphs (KG) are widely used abstractions to represent entity-centric knowledge. Approaches to embed entities, entity types and relations represented in the graph into vector spaces - often referred to as KG embeddings - have become …
The automatic detection of figurative language, such as irony and sarcasm, is one of the most challenging tasks of Natural Language Processing (NLP). This is because machine learning methods can be easily misled by the presence of words that have a …
The growing availability of social media platforms, in particular microblogs such as Twitter, opened new way to people for expressing their opinions. Sentiment Analysis aims at inferring the polarity of these opinions, but most of the existing …