Integrated legal information retrieval

In the last decade, lawyers have come to rely on digital information sources in almost every aspect of their work. Traditional information sources such as books and journals have to a large extend been replaced by their digital counterparts. Many law firms have already responded to this development and have abandoned their paper libraries in whole or in part. It is essential both to improve education with respect to the use of advanced disclosure systems for digital legal content, and to continue efforts to make these systems – not only the basic functions, but also the most powerful options – easier and more straightforward to use. Examples of both are given in this contribution

Multimodal Legal Information Retrieval

The goal of this thesis is to present a multifaceted way of inducing semantic representation from legal documents as well as accessing information in a precise and timely manner. The thesis explored approaches for semantic information retrieval (IR) in the Legal context with a technique that maps specific parts of a text to the relevant concept. This technique relies on text segments, using the Latent Dirichlet Allocation (LDA), a topic modeling algorithm for performing text segmentation, expanding the concept using some Natural Language Processing techniques, and then associating the text segments to the concepts using a semi-supervised text similarity technique. This solves two problems, i.e., that of user specificity in formulating query, and information overload, for querying a large document collection with a set of concepts is more fine-grained since specific information, rather than full documents is retrieved. The second part of the thesis describes our Neural Network Relevance Model for E-Discovery Information Retrieval. Our algorithm is essentially a feature-rich Ensemble system with different component Neural Networks extracting different relevance signal. This model has been trained and evaluated on the TREC Legal track 2010 data. The performance of our models across board proves that it capture the semantics and relatedness between query and document which is important to the Legal Information Retrieval domain

Integrated Legal Information Retrieval; new developments and educational challenges

The amount of legal information, available digitally, has increased gradually in the past three decades. We are now approaching a situation in which practically all legal information a lawyer needs on a daily basis can be obtained from digital sources. At the same time, powerful retrieval systems capable of integrating these sources and performing more effective search operations have become available. In this paper, new possibilities are outlined that have emerged now that such a large proportion of legal resources have been combined in unified collections. Also, the need to incorporate more advanced ‘legal information skills’ in the legal curriculum is discussed. These skills are required to ensure that all newly educated lawyers will be able to use digital legal information optimally, now and in the future

On the concept of relevance in legal information retrieval

The concept of 'relevance' is crucial to legal information retrieval, but because of its intuitive understanding it goes undefined too easily and unexplored too often. We discuss a conceptual framework on relevance within legal information retrieval, based on a typology of relevance dimensions used within general information retrieval science, but tailored to the specific features of legal information. This framework can be used for the development and improvement of legal information retrieval systems