In this work, we provide a broad overview of the distinct stages of E-Discovery. We portray them as an interconnected, often complex workflow process, while relating them to the general Electronic Discovery Reference Model (EDRM). We start with the definition of E-Discovery. We then describe the very positive role that NIST’s Text REtrieval Conference (TREC) has added to the science of E-Discovery, in terms of the tasks involved and the evaluation of the legal discovery work performed. Given the critical (...) nature that data analysis plays at various stages of the process, we present a pyramid model, which complements the EDRM model: for gathering and hosting; indexing; searching and navigating; and finally consolidating and summarizing E-Discovery findings. Next we discuss where the current areas of need and areas of growth appear to be, using one of the field’s most authoritative surveys of providers and consumers of E-Discovery products and services. We subsequently address some areas of Artificial Intelligence, both Information Retrieval-related and not, which promise to make future contributions to the E-Discovery discipline. Some of these areas include data mining applied to e-mail and social networks, classification and machine learning, and the technologies that will enable next generation E-Discovery. The lesson we convey is that the more IR researchers and others understand the broader context of E-Discovery, including the stages that occur before and after primary search, the greater will be the prospects for broader solutions, creative optimizations and synergies yet to be tapped. (shrink)

I develop and defend a novel answer to Peter van Inwagen’s ‘Special Composition Question,’ (SCQ) namely, under what conditions do some things compose and object? My answer is that things will compose an object when and only when they exist simultaneously relative to a reference frame (I call this ‘Temporally Restricted Composition’ or TREC). I then show how this view wards off objections given to ‘Unrestricted Mereology’ (UM). TREC, unlike other theories of Restricted Composition, does not fall prey (...) to worries about vagueness, anthropocentrism, or arbitrariness. TREC also has advantages over all the other answers to the SCQ. TREC is an account an A-theorist anti-Eternalist who wants an unrestricted mereology should accept. I also engage in some conceptual hygiene by showing how UM, as it should be used, should not, in itself, entail or contain a commitment to either Eternalism or Four-Dimensionalism. (shrink)

Retrieval of relevant unstructured information from the ever-increasing textual communications of individuals and businesses has become a major barrier to effective litigation/defense, mergers/acquisitions, and regulatory compliance. Such e-discovery requires simultaneously high precision with high recall (high-P/R) and is therefore a prototype for many legal reasoning tasks. The requisite exhaustive information retrieval (IR) system must employ very different techniques than those applicable in the hyper-precise, consumer search task where insignificant recall is the accepted norm. We apply Russell, et al.’s cognitive task (...) analysis of sensemaking by intelligence analysts to develop a semi-autonomous system that achieves high IR accuracy of F1 ≥ 0.8 compared to F1 < 0.4 typical of computer-assisted human-assessment (CAHA) or alternative approaches such as Roitblat, et al.’s. By understanding the ‘Learning Loop Complexes’ of lawyers engaged in successful small-scale document review, we have used socio-technical design principles to create roles, processes, and technologies for scalable human-assisted computer-assessment (HACA). Results from the NIST-TREC Legal Track’s interactive task from both 2008 and 2009 validate the efficacy of this sensemaking approach to the high-P/R IR task. (shrink)

One of the most active areas of research in semi-supervised learning has been to study methods for constructing good ensembles of classifiers. Ensemble systems are techniques that create multiple models and then combine them to produce improved results. These systems usually produce more accurate solutions than a single model would. Specially, multi-view ensemble systems improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than the (...) single-view approaches, document datasets often have no natural multiple views available. This study proposes an algorithm to generate a synthetic view from a standard text dataset. The model generates a new view from the standard bag-of-words approach using an algorithm based on hidden Markov models. To show the effectiveness of the proposed HMM-based synthetic view generation method, it has been integrated in a co-training ensemble system and tested with four text corpora: Reuters, 20 Newsgroup, TREC Genomics and OHSUMED. The results obtained are promising, showing a significant increase in the efficiency of the ensemble system compared to a single-view approach. (shrink)

Information Retrieval systems benefit from the use of long queries containing a large volume of search-relevant information. This situation is not common, as users of such systems tend to use very short and precise queries with few keywords. In this work we propose a modification of the Latent Dirichlet Allocation (LDA) technique using data from the document collection and its vocabulary for a better representation of short queries. Additionally, a study is carried out on how the modification of the proposed (...) LDA weighted vectors increase the performance using relevant documents as feedback. The work shown in this paper is tested using three biomedical corpora (TREC Genomics 2004, TREC Genomics 2005 and OHSUMED) and one legal corpus (FIRE 2017). Results prove that the application of the proposed representation technique, as well as the feedback adjustment, clearly outperforms the baseline methods (BM25 and non-modified LDA). (shrink)

Straipsnyje, remiantis pusiau struktūruotų biografinių interviu duomenimis, nagrinėjama lietuvių diasporos Kazachstane antrosios ir trečiosios kartų atmintis ir jos naudojimas jų etninės tapatybės daryboje. Po sovietmečiu nutrūkusio atminties perdavimo iš kartos į kartą ši atmintis daugiausia atkuriama kaip šeimų atmintis ir dėl vyraujančios „paveldimos“ etninės tapatybės sampratos jos atkūrimas vyko ir vyksta drauge su lietuviškosios tapatybės atkūrimu. Šeimų atminties atkūrimo emociniai poreikiai palikuonių kartose sustiprina ir etninės tapatybės atkūrimo poreikį, kai kuriais atvejais ši atmintis tampa svarbiu jos ištekliu.