By Giulia Venturi (auth.), Enrico Francesconi, Simonetta Montemagni, Wim Peters, Daniela Tiscornia (eds.)
The previous few years have noticeable a starting to be physique of study and perform addressing elements corresponding to computerized felony reasoning and argumentation, semantic and cross-language felony details retrieval, record category, criminal drafting, felony wisdom discovery and extraction. This cutting-edge Survey includes invited contributions of best researchers and teams eminently energetic within the box, that have been complemented with chosen papers from the Workshop on Semantic Processing of criminal Texts, held in Marrakech, Morocco, in 2008, in the framework of the 6th foreign convention on Language assets and overview (LREC 2008). those guides replicate the cutting-edge in linguistic applied sciences, instruments and assets concentrating on the automated extraction of correct info from criminal texts, and the based association of this extracted wisdom for criminal wisdom illustration and scholarly job, with specific emphasis at the an important function performed by means of language assets and human language applied sciences. The contents are geared up in 3 topical sections on info extraction; development of information assets; and semantic indexing, summarization and translation.
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Extra resources for Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language
This feature compares city-state information in the authority ﬁle record with city-state information in the text structured record. 0 if the states mismatch. Note that we consider all city-state pairs that co-occur in the same paragraph with the attorney name and use the highest scoring city-state pair to set our feature function value. Firm name similarity function. This feature measures the TFIDF cosine similarity between the law ﬁrm name speciﬁed in the authority ﬁle record and law ﬁrm names in the text structured record.
We measure recall by dividing the number of correct positive matches made by the number of positive instances manually identiﬁed. e. e. co-referring) pair which has been identiﬁed. One method of creating test data is to randomly select structured records extracted from the text and manually match them to records in the authority ﬁle. Typically, we want to have at least 300 positive test record pairs. For our example attorney NE resolution system, we created 500 manual matches between Named Entity Recognition and Resolution in Legal Text 39 Table 4.
The operation of particular feature functions is governed by the semantics of the ﬁelds being compared. Two types of similarity functions merit special mention because they work well for many ﬁeld classes. They are the TFIDF cosine similarity function and the family of string edit distance functions. The TFIDF cosine similarity function computes the cosine similarity of a string ﬁeld in the text structure record and a string ﬁeld value in the authority record. The inverse document frequency values for words in the record ﬁeld are computed from the ﬁeld in the authority ﬁle.
Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language by Giulia Venturi (auth.), Enrico Francesconi, Simonetta Montemagni, Wim Peters, Daniela Tiscornia (eds.)