Paolo Rosso

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Paolo Rosso is an author.

Publications

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
On the Use of PU Learning for Quality Flaw Prediction in Wikipedia PAN English 2012 In this article we describe a new approach to assess Quality Flaw Prediction in Wikipedia. The partially supervised method studied, called PU Learning, has been successfully applied in classifications tasks with traditional corpora like Reuters-21578 or 20-Newsgroups. To the best of our knowledge, this is the first time that it is applied in this domain. Throughout this paper, we describe how the original PU Learning approach was evaluated for assessing quality flaws and the modifications introduced to get a quality flaws predictor which obtained the best F1 scores in the task “Quality Flaw Prediction in Wikipedia” of the PAN challenge. 0 1
Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features Wikipedia
Wiki
Collaboration
Vandalism
Machine learning
Metadata
Natural Language Processing
Reputation
Lecture Notes in Computer Science English February 2011 Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions. 0 1
Extracción de Corpus Paralelos de la Wikipedia basada en la Obtención de Alineamientos Bilingües a Nivel de Frase Comparable corpora
Parallel sentences extraction
Machine translation
Proceedings of the Workshop on Iberian Cross-Language Natural Language Processing Tasks (ICL 2011) Spanish 2011 This paper presents a proposal for extracting parallel corpora from Wikipedia on the basis of statistical machine translation techniques. We have used word-level alignment models from IBM in order to obtain phrase-level bilingual alignments between documents pairs. We have manually annotated a set of test English-Spanish comparable documents in order to evaluate the model. The obtained results are encouraging. 4 0
Wikipedia vandalism detection: Combining natural language, metadata, and reputation features Lecture Notes in Computer Science English 2011 Wikipedia is an online encyclopedia which anyone can edit. While most edits are constructive, about 7% are acts of vandalism. Such behavior is characterized by modifications made in bad faith; introducing spam and other inappropriate content. In this work, we present the results of an effort to integrate three of the leading approaches to Wikipedia vandalism detection: a spatio-temporal analysis of metadata (STiki), a reputation-based system (WikiTrust), and natural language processing features. The performance of the resulting joint system improves the state-of-the-art from all previous methods and establishes a new baseline for Wikipedia vandalism detection. We examine in detail the contribution of the three approaches, both for the task of discovering fresh vandalism, and for the task of locating vandalism in the complete set of Wikipedia revisions. 0 1
Cross-language plagiarism detection Language Resources and Evaluation 2010 0 0
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