Melboune at the TREC 2011 Legal Track

نویسندگان

  • William Webber
  • Phil Farrelly
چکیده

The Melbourne team was a collaboration of the University of Melbourne, RMIT University, and the Victorian Society for Computers and the Law. The approach taken was to train a support vector machine based upon textual features using active learning. Two sources of relevance annotations were used for different runs: the official annotations, provided by the topic authorities; and annotations provided by a member of the Melbourne team with e-discovery experience (though not legal training). We describe the SVM method used in Section 1.1, the run using official annotations in Section 1.2, and the run using the internal annotations in Section 1.3.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

PRIS at TREC 2011 Legal Track Discovery Based on Relevant Feedback

In order to finish the task of TREC 2011 Legal Track, this paper puts forward an experiment method, which combines indri and relevant feedback to evaluate the probability of relevance of every document in a collection.

متن کامل

University of Waterloo at TREC 2011: A Social Networking Approach to the Legal Learning Track

This paper reports on the University of Waterloo experience with the Legal Learning track where three different methods were used to approach the retrieval task. Two are based on previously used methods and the last is a novel method based on modifying the responsiveness probability using social network analysis.

متن کامل

Learning Task Experiments in the TREC 2011 Legal Track

The Learning Task of the TREC 2011 Legal Track investigated the effectiveness of e-Discovery search techniques at selecting training examples and learning from them to estimate the probability of relevance of every document in a collection. The task specified 3 test topics, each of which included a one-sentence request for documents to produce from a target collection of 685,592 e-mail messages...

متن کامل

Cluster-Based Relevance Feedback: Legal Track 2011

This is our second participation in the TREC Legal Track. The TREC Legal Track 2011 featured only the Learning Task. We participated in Topics 401 and 403. We used Lemur 4.11 for Boolean retrieval and followed it with a clustering technique, where we chose members from each cluster (which we called seeds) for relevance judgement by the TA and assumed all other members of the cluster whose seeds...

متن کامل

Overview of the TREC 2011 Legal Track

The TREC 2011 Legal Track consisted of a single task: the learning task, which captured elements of both the TREC 2010 learning and interactive tasks. Participants were required to rank the entire corpus of 685,592 documents by their estimate of the probability of responsiveness to each of three topics, and also to provide a quantitative estimate of that probability. Participants were permitted...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011