Davide Buscaldi

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Davide Buscaldi is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
A Bag-of-Words Based Ranking Method for the Wikipedia Question Answering Task Evaluation of Multilingual and Multi-modal Information Retrieval English 2007 This paper presents a simple approach to the Wikipedia Question Answering pilot task in CLEF 2006. The approach ranks the snippets, retrieved using the Lucene search engine, by means of a similarity measure based on bags of words extracted from both the snippets and the articles in wikipedia. Our participation was in the monolingual English and Spanish tasks. We obtained the best results in the Spanish one. 0 0
A comparison of methods for the automatic identification of locations in Wikipedia English 2007 In this paper we compare two methods for the automatic identification of geographical articles in encyclopedic resources such as Wikipedia. The methods are a WordNet-based method that uses a set of keywords related to geographical places, and a multinomial Naïve Bayes classificator, trained over a randomly selected subset of the English Wikipedia. This task may be included into the broader task of Named Entity classification, a well-known problem in the field of Natural Language Processing. The experiments were carried out considering both the full text of the articles and only the definition of the entity being described in the article. The obtained results show that the information contained in the page templates and the category labels is more useful than the text of the articles. 0 0
Some Experiments in Humour Recognition Using the Italian Wikiquote Collection Lecture Notes in Computer Science English 2007 In this paper we present some results obtained in humour classification over a corpus of Italian quotations manually extracted and tagged from the Wikiquote project. The experiments were carried out using both a multinomial Naïve Bayes classifier and a Support Vector Machine (SVM). The considered features range from single words to n-grams and sentence length. The obtained results show that it is possible to identify the funny quotes even with the simplest features (bag of words); the bayesian classifier performed better than the SVM. However, the size of the corpus size is too small to support definitive assertions. 0 0