NAVDEX, a helpful tool for the classification of environmental legislation

Since its launch in 1998 the thematic indexation of the Flemish Environmental Navigator is carried out manually by legal experts of the University of Ghent, Belgium. However, due to the exponential growth of legal documents a physical indexation process eventually was no longer tenable, nor desirable. Hence, a semi-automatic indexing tool for environmental legislation, called NAVDEX, was developed. A specific algorithm was determined, based On the presence of similar terms in law objects. A parameter was defined, reflecting the strength of the relation between law objects in order to computerise the return on a user's query. 1/7 view, of managing the relations between law objects, a visualisation tool was created in order to provide the legal experts with a detailed overview of all associated law Objects. The testing corpus was decided to be VLAREA, a Flemish order concerning waste prevention and management. The evaluation of the test results was carried out by experts in environmental legislation, who computed the relative recall of several search terms. With an average score of 0.63 NAVDEX is able to retrieve nearly two third of the associated law objects. Consequently the evaluators' conclusions were unanimous so as to define NAVDEX as a useful tool to determine and visualise associated LawObjects

An Experimental Digital Library Platform - A Demonstrator Prototype for the DigLib Project at SICS

Within the framework of the Digital Library project at SICS, this thesis describes the implementation of a demonstrator prototype of a digital library (DigLib); an experimental platform integrating several functions in one common interface. It includes descriptions of the structure and formats of the digital library collection, the tailoring of the search engine Dienst, the construction of a keyword extraction tool, and the design and development of the interface. The platform was realised through sicsDAIS, an agent interaction and presentation system, and is to be used for testing and evaluating various tools for information seeking. The platform supports various user interaction strategies by providing: search in bibliographic records (Dienst); an index of keywords (the Keyword Extraction Function (KEF)); and browsing through the hierarchical structure of the collection. KEF was developed for this thesis work, and extracts and presents keywords from Swedish documents. Although based on a comparatively simple algorithm, KEF contributes by supplying a long-felt want in the area of Information Retrieval. Evaluations of the tasks and the interface still remain to be done, but the digital library is very much up and running. By implementing the platform through sicsDAIS, DigLib can deploy additional tools and search engines without interfering with already running modules. If wanted, agents providing other services than SICS can supply, can be plugged in

Information Retrieval Models

Many applications that handle information on the internet would be completely\ud inadequate without the support of information retrieval technology. How would\ud we find information on the world wide web if there were no web search engines?\ud How would we manage our email without spam filtering? Much of the development\ud of information retrieval technology, such as web search engines and spam\ud filters, requires a combination of experimentation and theory. Experimentation\ud and rigorous empirical testing are needed to keep up with increasing volumes of\ud web pages and emails. Furthermore, experimentation and constant adaptation\ud of technology is needed in practice to counteract the effects of people that deliberately\ud try to manipulate the technology, such as email spammers. However,\ud if experimentation is not guided by theory, engineering becomes trial and error.\ud New problems and challenges for information retrieval come up constantly.\ud They cannot possibly be solved by trial and error alone. So, what is the theory\ud of information retrieval?\ud There is not one convincing answer to this question. There are many theories,\ud here called formal models, and each model is helpful for the development of\ud some information retrieval tools, but not so helpful for the development others.\ud In order to understand information retrieval, it is essential to learn about these\ud retrieval models. In this chapter, some of the most important retrieval models\ud are gathered and explained in a tutorial style

Towards an Information Retrieval Theory of Everything

I present three well-known probabilistic models of information retrieval in tutorial style: The binary independence probabilistic model, the language modeling approach, and Google's page rank. Although all three models are based on probability theory, they are very different in nature. Each model seems well-suited for solving certain information retrieval problems, but not so useful for solving others. So, essentially each model solves part of a bigger puzzle, and a unified view on these models might be a first step towards an Information Retrieval Theory of Everything

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

DCU@INEX-2012: exploring sentence retrieval for tweet contextualization

For the participation of Dublin City University (DCU) in the INEX-2012 tweet contextualization task, we investigated sentence retrieval methodologies. The task requires providing the context to an ad-hoc real-life tweet. This context is to be constructed from Wikipedia articles. Our approach involves indexing the passages in Wikipedia articles as separate retrievable units, extracting sentences from the top ranked passages, computing the sentence selection score for each such sentence with respect to the query, and then returning the top most similar ones. The simple sentence selection strategy performed quite well in the task. Our best run has ranked rst from the readability perspective and ranked eighth as ordered by informativeness out of 33 ocial runs