Giuseppe Attardi

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Giuseppe Attardi is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
The Tanl lemmatizer enriched with a sequence of cascading filters Deep Search
Part-of-Speech tagging
Lecture Notes in Computer Science English 2013 We have extended an existing lemmatizer, which relies on a lexicon of about 1.2 millions form, where lemmas are indexed by rich PoS tags, with a sequence of cascading filters, each one in charge of dealing with specific issues related to out-of-dictionary words. The last two filters are devoted to resolve semantic ambiguities between words of the same syntactic category, by querying external resources: an enriched index built on the Italian Wikipedia and the Google index. 0 0
Semantically Annotated Snapshot of the English Wikipedia LREC'08 2008 This paper describes SW1, the first version of a semantically annotated snapshot of the English Wikipedia. In recent years Wikipedia has become a valuable resource for both the Natural Language Processing (NLP) community and the Information Retrieval (IR) community. Although NLP technology for processing Wikipedia already exists, not all researchers and developers have the computational resources to process such a volume of information. Moreover, the use of different versions of Wikipedia processed differently might make it difficult to compare results. The aim of this work is to provide easy access to syntactic and semantic annotations for researchers of both NLP and IR communities by building a reference corpus to homogenize experiments and make results comparable. These resources, a semantically annotated corpus and a “entity containment” derived graph, are licensed under the GNU Free Documentation License and available from 0 1
Ranking Very Many Typed Entities on Wikipedia CIKM '07: Proceedings of the Sixteenth ACM International Conference on Information and Knowledge Management 2007 We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very speci??c. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval. 0 0
Ranking very many typed entities on Wikipedia English 2007 0 0