Entity Linking for Italian Tweets
Authors: Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro (Università di Bari Aldo Moro, 2015)
Topic: Lexical and Semantic Resources and Analysis

Abstract
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 developed specifically for the Italian language.

Introduction
In this paper we address the problem of entity linking for Italian tweets. Named Entity Linking (NEL) is the task of annotating entity mentions in a portion of text with links to a knowledge base. This task usually requires as first step the recognition of portions of text that refer to named entities (entity recognition). The linking phase follows, which usually subsumes the entity disambiguation, i.e. selecting the proper concept from a restricted set of candidates (e.g. New York city or New York state). NEL together with Word Sense Disambiguation, i.e. the task of associating each word occurrence with its proper meaning given a sense inventory, is critical to enable automatic systems to make sense of unstructured text. Initially developed for reasonably long and clean text, such as news articles, NEL techniques usually show unsatisfying performance on noisy, short and poorly written text constituted by microblogs such as Twitter. These difficulties notwithstanding, with an average of 500 billion posts being generated every day1 , tweets represent a rich source of information. Twitterbased tasks like user interest discovery, tweet recommendation, social/economical analysis, and so forth, could benefit from such a kind of semantic features represented by named entities linked to a knowledge base. Such tasks become even more problematic when the tweet analysis involves languages different from English. Specifically, in the context of Italian language, the lack of languagespecific resources and annotated tweet datasets complicates the assessment of NEL algorithms for tweets. Our main contributions to this problem are: • An adaptation of a Twitter-based NEL algorithm based on a Distributional Semantic Model (DSM-TEL), which needs no specific Italian resources since it is completely unsupervised (Section 3). • An Italian dataset of manually annotated tweets for NEL. To the best of our knowledge, this is the first Italian dataset of such a type. Section 2 reports details concerning the annotation phase and statistics about the dataset. • An evaluation of well known NEL algorithms available for Italian language on this dataset, comparing their performance with our DSMTEL algorithm in terms of both entity recognition and linking. Section 4 shows and analyses the results of that evaluation.

Conclusion
We tackled the problem of entity linking for Italian tweets. Our contribution is threefold: 1) we build a first Italian tweet dataset for entity linking, 2) we adapted a distributional-based NEL algorithm to the Italian language, and 3) we compared stateof-the-art systems on the built dataset. As for English, the entity linking task for Italian tweets turn out to be quite difficult, as pointed out by the very low performance of all systems employed. As future work we plan to extend the dataset in order to provide more examples for training and testing data.