The coming of age of legal technology:What will be needed to take Legal Tech to a new level?

Many useful applications have been developed over the years for legal research and documentation. Technical opportunities are more extensive than they were two or three decades ago. Legal sources are certainly more accessible now and more diverse. Documents no longer consist of fixed series of characters only, but may interact with their users. Computer applications for legal practice, part of the broader ‘Legal Tech’ concept, are gaining popularity amongst lawyers. It is, therefore, interesting to examine what the present possibilities of Legal Tech are now, and also what the future may hold.Application types can be distinguished by the complexity (‘intelligence’) of the processing involved and by the degree of influence a user has on the output. Decision support systems and programmed decision systems can be quite intelligent, but differ in the degree of user input. For the fully intelligent programs that do not require much user input, there is the question of explainability. To ‘feed’ as well as assess these programs, jurimetrics research is necessary. Jurimetrics is the empirical, usually quantitative, study of law. By means of jurimetrics research, legal decisions can be analysed and predicted.Given all this, can computers already take over decision-making in the field of law? Although building (‘artificially intelligent’; ‘robot-’) applications containing self-learning algorithms is in itself possible these days, that does not mean these programs can match human decision-making or sufficiently explain and justify attained results. As it is a function of the law not only to build on existing legal dogmas but also to keep in step with developments in society, decisions may be needed that are essentially different from those taken in the past. Legal decision-making is a creative process that requires emotional skills. At present there are still technological limitations as well as numerous practical and theoretical problems to really replicate human decision-making. To overcome these, we argue that a new phase of technological development would be necessary, offering fundamentally new possibilities. For the foreseeable future, therefore, the conclusion must be that handing over legal decision-making to computers is not desirable. <br/

Review of Research on Speech Technology: Main Contributions From Spanish Research Groups

In the last two decades, there has been an important increase in research on speech technology in Spain, mainly due to a higher level of funding from European, Spanish and local institutions and also due to a growing interest in these technologies for developing new services and applications. This paper provides a review of the main areas of speech technology addressed by research groups in Spain, their main contributions in the recent years and the main focus of interest these days. This description is classified in five main areas: audio processing including speech, speaker characterization, speech and language processing, text to speech conversion and spoken language applications. This paper also introduces the Spanish Network of Speech Technologies (RTTH. Red Temática en Tecnologías del Habla) as the research network that includes almost all the researchers working in this area, presenting some figures, its objectives and its main activities developed in the last years

Automatic bilingual text document summarization.

Lo Sau-Han Silvia.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 137-143).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Definition of a summary --- p.2Chapter 1.2 --- Definition of text summarization --- p.3Chapter 1.3 --- Previous work --- p.4Chapter 1.3.1 --- Extract-based text summarization --- p.5Chapter 1.3.2 --- Abstract-based text summarization --- p.8Chapter 1.3.3 --- Sophisticated text summarization --- p.9Chapter 1.4 --- Summarization evaluation methods --- p.10Chapter 1.4.1 --- Intrinsic evaluation --- p.10Chapter 1.4.2 --- Extrinsic evaluation --- p.11Chapter 1.4.3 --- The TIPSTER SUMMAC text summarization evaluation --- p.11Chapter 1.4.4 --- Text Summarization Challenge (TSC) --- p.13Chapter 1.5 --- Research contributions --- p.14Chapter 1.5.1 --- Text summarization based on thematic term approach --- p.14Chapter 1.5.2 --- Bilingual news summarization based on an event-driven approach --- p.15Chapter 1.6 --- Thesis organization --- p.16Chapter 2 --- Text Summarization based on a Thematic Term Approach --- p.17Chapter 2.1 --- System overview --- p.18Chapter 2.2 --- Document preprocessor --- p.20Chapter 2.2.1 --- English corpus --- p.20Chapter 2.2.2 --- English corpus preprocessor --- p.22Chapter 2.2.3 --- Chinese corpus --- p.23Chapter 2.2.4 --- Chinese corpus preprocessor --- p.24Chapter 2.3 --- Corpus thematic term extractor --- p.24Chapter 2.4 --- Article thematic term extractor --- p.26Chapter 2.5 --- Sentence score generator --- p.29Chapter 2.6 --- Chapter summary --- p.30Chapter 3 --- Evaluation for Summarization using the Thematic Term Ap- proach --- p.32Chapter 3.1 --- Content-based similarity measure --- p.33Chapter 3.2 --- Experiments using content-based similarity measure --- p.36Chapter 3.2.1 --- English corpus and parameter training --- p.36Chapter 3.2.2 --- Experimental results using content-based similarity mea- sure --- p.38Chapter 3.3 --- Average inverse rank (AIR) method --- p.59Chapter 3.4 --- Experiments using average inverse rank method --- p.60Chapter 3.4.1 --- Corpora and parameter training --- p.61Chapter 3.4.2 --- Experimental results using AIR method --- p.62Chapter 3.5 --- Comparison between the content-based similarity measure and the average inverse rank method --- p.69Chapter 3.6 --- Chapter summary --- p.73Chapter 4 --- Bilingual Event-Driven News Summarization --- p.74Chapter 4.1 --- Corpora --- p.75Chapter 4.2 --- Topic and event definitions --- p.76Chapter 4.3 --- Architecture of bilingual event-driven news summarization sys- tem --- p.77Chapter 4.4 --- Bilingual event-driven approach summarization --- p.80Chapter 4.4.1 --- Dictionary-based term translation applying on English news articles --- p.80Chapter 4.4.2 --- Preprocessing for Chinese news articles --- p.89Chapter 4.4.3 --- Event clusters generation --- p.89Chapter 4.4.4 --- Cluster selection and summary generation --- p.96Chapter 4.5 --- Evaluation for summarization based on event-driven approach --- p.101Chapter 4.6 --- Experimental results on event-driven summarization --- p.103Chapter 4.6.1 --- Experimental settings --- p.103Chapter 4.6.2 --- Results and analysis --- p.105Chapter 4.7 --- Chapter summary --- p.113Chapter 5 --- Applying Event-Driven Summarization to a Parallel Corpus --- p.114Chapter 5.1 --- Parallel corpus --- p.115Chapter 5.2 --- Parallel documents preparation --- p.116Chapter 5.3 --- Evaluation methods for the event-driven summaries generated from the parallel corpus --- p.118Chapter 5.4 --- Experimental results and analysis --- p.121Chapter 5.4.1 --- Experimental settings --- p.121Chapter 5.4.2 --- Results and analysis --- p.123Chapter 5.5 --- Chapter summary --- p.132Chapter 6 --- Conclusions and Future Work --- p.133Chapter 6.1 --- Conclusions --- p.133Chapter 6.2 --- Future work --- p.135Bibliography --- p.137Chapter A --- English Stop Word List --- p.144Chapter B --- Chinese Stop Word List --- p.149Chapter C --- Event List Items on the Corpora --- p.151Chapter C.1 --- "Event list items for the topic ""Upcoming Philippine election""" --- p.151Chapter C.2 --- "Event list items for the topic ""German train derail"" " --- p.153Chapter C.3 --- "Event list items for the topic ""Electronic service delivery (ESD) scheme"" " --- p.154Chapter D --- The sample of an English article (9505001.xml). --- p.15

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Automated Identification of Digital Evidence across Heterogeneous Data Resources

Digital forensics has become an increasingly important tool in the fight against cyber and computer-assisted crime. However, with an increasing range of technologies at people’s disposal, investigators find themselves having to process and analyse many systems with large volumes of data (e.g., PCs, laptops, tablets, and smartphones) within a single case. Unfortunately, current digital forensic tools operate in an isolated manner, investigating systems and applications individually. The heterogeneity and volume of evidence place time constraints and a significant burden on investigators. Examples of heterogeneity include applications such as messaging (e.g., iMessenger, Viber, Snapchat, and WhatsApp), web browsers (e.g., Firefox and Google Chrome), and file systems (e.g., NTFS, FAT, and HFS). Being able to analyse and investigate evidence from across devices and applications in a universal and harmonized fashion would enable investigators to query all data at once. In addition, successfully prioritizing evidence and reducing the volume of data to be analysed reduces the time taken and cognitive load on the investigator. This thesis focuses on the examination and analysis phases of the digital investigation process. It explores the feasibility of dealing with big and heterogeneous data sources in order to correlate the evidence from across these evidential sources in an automated way. Therefore, a novel approach was developed to solve the heterogeneity issues of big data using three developed algorithms. The three algorithms include the harmonising, clustering, and automated identification of evidence (AIE) algorithms. The harmonisation algorithm seeks to provide an automated framework to merge similar datasets by characterising similar metadata categories and then harmonising them in a single dataset. This algorithm overcomes heterogeneity issues and makes the examination and analysis easier by analysing and investigating the evidential artefacts across devices and applications based on the categories to query data at once. Based on the merged datasets, the clustering algorithm is used to identify the evidential files and isolate the non-related files based on their metadata. Afterwards, the AIE algorithm tries to identify the cluster holding the largest number of evidential artefacts through searching based on two methods: criminal profiling activities and some information from the criminals themselves. Then, the related clusters are identified through timeline analysis and a search of associated artefacts of the files within the first cluster. A series of experiments using real-life forensic datasets were conducted to evaluate the algorithms across five different categories of datasets (i.e., messaging, graphical files, file system, internet history, and emails), each containing data from different applications across different devices. The results of the characterisation and harmonisation process show that the algorithm can merge all fields successfully, with the exception of some binary-based data found within the messaging datasets (contained within Viber and SMS). The error occurred because of a lack of information for the characterisation process to make a useful determination. However, on further analysis, it was found that the error had a minimal impact on subsequent merged data. The results of the clustering process and AIE algorithm showed the two algorithms can collaborate and identify more than 92% of evidential files.HCED Ira