We present the first report of automatic sentiment summarization in the legal domain. This work is based on processing a set of legal questions with a system consisting of a semi-automatic Web blog search module and FastSum, a fully automatic extractive multi-document sentiment summarization system. We provide quantitative evaluation results of the summaries using legal expert reviewers. We report baseline evaluation results for query-based sentiment summarization for legal blogs: on a five-point scale, average responsiveness and linguistic quality are slightly higher than 2 (with human inter-rater agreement at k = 0.75). To the best of our knowledge, this is the first evaluation of sentiment summarization in the legal blogosphere.

Medical terms occur across a wide variety of legal, medical, and news corpora. Documents containing these terms are of particular interest to legal professionals operating in such fields as medical malpractice, personal injury, and product liability. This paper describes a novel method of tagging medical terms in legal, medical, and news text that is very fast and also has high recall and precision. To date, most research in medical term spotting has been confined to medical text and has approached the problem by extracting noun phrases from sentences and mapping them to a list of medical concepts via a fuzzy lookup. The medical term tagging described in this paper relies on a fast finite state machine that finds within sentences the longest contiguous sets of words associated with medical terms in a medical term authority file, converts word sets into medical term hash keys, and looks up medical concept ids associated with the hash keys. Additionally our system relies on a probabilistic term classifier that uses local context to disambiguate terms being used in a medical sense from terms being used in a non-medical sense. Our method is two orders of magnitude faster than an approach based on noun phrase extraction and has better precision and recall for terms pertaining to injuries, diseases, drugs, medical procedures, and medical devices. The methods presented here have been implemented and are the core engines for a Thomson West product called the Medical Litigator. Thus far, the Medical Litigator has processed over 100 million documents and generated over 165 million tags representing approximately 164,000 unique medical concepts. The resulting system is very fast and posted a recall from 0.79 to 0.93 and precision between 0.94 and 0.97, depending on the document type.

Document clustering is an important tool for applications such as Web search engines. Clustering documents enables the user to have a good overall view of the information contained in the documents that he has. However, existing algorithms suffer from various aspects, hard clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document, while soft clustering algorithms (where each document can belong to multiple clusters) are usually inefficient. We propose SISC (similarity-based soft clustering), an efficient soft clustering algorithm based on a given similarity measure. SISC requires only a similarity measure for clustering and uses randomization to help make the clustering efficient. Comparison with existing hard clustering algorithms like K-means and its variants shows that SISC is both effective and efficient.