Using Embeddings for Both Entity Recognition and Linking in Tweets

L’articolo descrive la nostra partecipazione al task di Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) a Evalita 2016. Il nostro approccio si basa sull’utilizzo di un Named Entity tagger che sfrutta embeddings sia character-level che word-level. I primi consentono di apprendere le idiosincrasie della scrittura nei tweet. L’uso di un tagger completo consente di riconoscere uno spettro più ampio di entità rispetto a quelle conosciute per la loro presenza in Knowledge Base o gazetteer. Le prove sottomesse hanno ottenuto il primo, secondo e quarto dei punteggi ufficiali.The paper describes our sub-missions to the task on Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) at Evalita 2016. Our approach relies on a technique of Named Entity tagging that exploits both charac-ter-level and word-level embeddings. Character-based embeddings allow learn-ing the idiosyncrasies of the language used in tweets. Using a full-blown Named Entity tagger allows recognizing a wider range of entities than those well known by their presence in a Knowledge Base or gazetteer. Our submissions achieved first, second and fourth top offi-cial scores

A Reverse Approach to Named Entity Extraction and Linking in Microposts

ABSTRACT In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base

Entity Linking for the Semantic Annotation of Italian Tweets

Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which exploits the similarity measure computed over a Distributional Semantic Model, in the context of Italian tweets. In order to evaluate the proposed algorithm, we introduce a new dataset of tweets for entity linking that we have developed specifically for the Italian language

EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop

Editor of the proceedings of EVALITA 2016