Conceptual search – ESI, litigation and the issue of language

Across the globe, legal, business and technical practitioners charged with managing information are continually challenged by rapid-fire evolution and growth in the legal and technology fields. In the United States, new compliance requirements, amendments to the Federal Rules of Civil Procedure (FRCP) and corresponding case law, along with technical advances, have made litigation support one of the most exciting professions in the legal arena. In the UK, revisions to the Practice Direction to CPR Rule 31 require parties in civil litigation to consider the impacts associated with electronic documents. One emerging technology trends—both aiding and complicating the management of electronically stored information (ESI) in litigation in the US, EU and UK alike—is the notion of “conceptual search.” This paper focuses on the evolution of conceptual search technology, and predictions of where this science will take legal professionals and technical information managers in coming years and a look at the advantages conceptual search can provide in dealing with the issue of language. This paper will focus primarily and the latent semantic analysis approach to conceptual search and why this approach is advantageous when searching ESI regardless of the language used in the documents, even to the extent of allowing for cross language searching and accurate searching of documents that contain co-mingle foreign terms with the native language

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems