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 …
This paper describes the framework proposed by the UNIMIB Team for the task of Named Entity Recognition and Linking of Italian tweets (NEEL-IT). The proposed pipeline, which represents an entry level system, is composed of three main steps: (1) Named …
Given the potential rise in the amount of user-generated content on social network, research efforts towards Information Extraction have significantly increased, giving leeway to the emergence of numerous *Named Entity Recognition* (NER) systems. …
Sentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text. However, most of the approaches in the state of the art usually investigate independently each aspect, i.e. Subjectivity Classification, …
Numerous state-of-the-art **Named Entity Recognition** (NER) systems use different classification schemas/ontologies. Comparisons and integration among NER systems, thus, becomes complex. In this paper, we propose a transfer-learning approach where …
In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and …
Real world applications of machine learning in natural language processing can span many different domains and usually require a huge effort for the annotation of domain specific training data. For this reason, domain adaptation techniques have …