Marco Fabbri


pdf bib
RIDIRE-CPI: an Open Source Crawling and Processing Infrastructure for Supervised Web-Corpora Building
Alessandro Panunzi | Marco Fabbri | Massimo Moneglia | Lorenzo Gregori | Samuele Paladini
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper introduces the RIDIRE-CPI, an open source tool for the building of web corpora with a specific design through a targeted crawling strategy. The tool has been developed within the RIDIRE Project, which aims at creating a 2 billion word balanced web corpus for Italian. RIDIRE-CPI architecture integrates existing open source tools as well as modules developed specifically within the RIDIRE project. It consists of various components: a robust crawler (Heritrix), a user friendly web interface, several conversion and cleaning tools, an anti-duplicate filter, a language guesser, and a PoS tagger. The RIDIRE-CPI user-friendly interface is specifically intended for allowing collaborative work performance by users with low skills in web technology and text processing. Moreover, RIDIRE-CPI integrates a validation interface dedicated to the evaluation of the targeted crawling. Through the content selection, metadata assignment, and validation procedures, the RIDIRE-CPI allows the gathering of linguistic data with a supervised strategy that leads to a higher level of control of the corpus contents. The modular architecture of the infrastructure and its open-source distribution will assure the reusability of the tool for other corpus building initiatives.


pdf bib
Integration of a Multilingual Keyword Extractor in a Document Management System
Andrea Agili | Marco Fabbri | Alessandro Panunzi | Manuel Zini
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we present a new Document Management System called DrStorage. This DMS is multi-platform, JCR-170 compliant, supports WebDav, versioning, user authentication and authorization and the most widespread file formats (Adobe PDF, Microsoft Office, HTML,...). It is also easy to customize in order to enhance its search capabilities and to support automatic metadata assignment. DrStorage has been integrated with an automatic language guesser and with an automatic keyword extractor: these metadata can be assigned automatically to documents, because the DrStorage’s server part has benn modified to allow that metadata assignment takes place as documents are put in the repository. Metadata can greatly improve the search capabilites and the results quality of a search engine. DrStorage’s client has been customized with two search results view: the first, called timeline view, shows temporal trends of queries as an histogram, the second, keyword cloud, shows which words are correlated and how much are correlated with the results of a particular day.


pdf bib
Integrating Methods and LRs for Automatic Keyword Extraction from Open Domain Texts
Alessandro Panunzi | Marco Fabbri | Massimo Moneglia
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The paper presents a tool for keyword extraction from multilingual resources developed within the AXMEDIS project. In this tool lexical collocations (Sinclair, 1991) internal to documents are used to enhance the performance obtained through standard statistical procedure. A first set of mono-term keywords is extracted through the TF.IDF algorithm (Salton, 1989). The internal analysis of the document generates a second set of multi-term keywords based on the first set, rather than on multi-term frequency comparison with a general resource (Witten et al. 1999). Collocations in which a mono-term keyword occurs as the head are considered as multi-term keywords, and are assumed to increase the identification of the content. The evaluation compares the results of the TF.IDF procedure and the ones obtained with the enhanced procedure in terms of “precision”. Each set of keywords received a value from the point of view of a possible user, regarding: (a) overall efficiency of the whole set of keywords for the identification of the content; (b) adequacy of each extracted keyword. Results show that multi-term keywords increase the content identification with a 100% relative factor and that the adequacy is enhanced in 33% of cases.