Vittoria Cozza

Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an "actionable" model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment.

This work presents the solution adopted by the sisinflab team to solve the task NEEL-IT (Named Entity rEcognition and Linking in Italian Tweets) at the Evalita 2016 challenge. The task consists in the annotation of each named entity mention in a Twitter message written in Italian, among characters, events, people, locations, organizations, products and things and the eventual linking when a corresponding entity is found in a knowledge base (e.g. DBpedia). We faced the challenge through an approach that combines unsupervised methods, such as DBpedia Spotlight and word embeddings, and supervised techniques such as a CRF classifier and a Deep learning classifier.