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Extra resources for 55 Successful Harvard Law School Application Essays: With Analysis by the Staff of The Harvard Crimson (2nd Edition)
We also created 70,000 positive instances automatically in our caselaw corpus by using rare ﬁrst and last name combinations as a surrogate feature. S. population and only once in the attorney authority ﬁle. Any attorney ﬁle and text ﬁle record pairs that have a matching rare ﬁrst-last name combination are considered positive matches. 8 Using Support Vector Machine to Learn Matching Function A good method of linking structured records to authority records is to use a Support Vector Machine (SVM) classiﬁer that measures the likelihood that a particular structured record and authority record refer to the same entity.
7. If the highest scoring feature vector exceeds a minimum threshold, assign the entity id associated with the matching authority ﬁle record to the text reference associated with the text structure record. We measured how well the SVM approach works using manual and automatically acquired training data for the attorney names in case law. Our results are shown in Table 5. The f-measure using our surrogate training approach was only three percentage points below our manual training approach. The diﬀerence was the six percentage point drop in recall.
Involved in a legal proceedings. Thus, being able to recognize which word sequences are names and resolve these names to what they refer is useful for many legal text processing tasks. In this paper we describe methods for named entity (NE) recognition and resolution in legal texts. To make this discussion more concrete, consider the text in Figure 1. SUPREME COURT OF THE UNITED STATES Syllabus MICROSOFT CORP. v. AT&T CORP. CERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE FEDERAL CIRCUIT No.