25 research outputs found
Users and Assessors in the Context of INEX: Are Relevance Dimensions Relevant?
The main aspects of XML retrieval are identified by analysing and comparing
the following two behaviours: the behaviour of the assessor when judging the
relevance of returned document components; and the behaviour of users when
interacting with components of XML documents. We argue that the two INEX
relevance dimensions, Exhaustivity and Specificity, are not orthogonal
dimensions; indeed, an empirical analysis of each dimension reveals that the
grades of the two dimensions are correlated to each other. By analysing the
level of agreement between the assessor and the users, we aim at identifying
the best units of retrieval. The results of our analysis show that the highest
level of agreement is on highly relevant and on non-relevant document
components, suggesting that only the end points of the INEX 10-point relevance
scale are perceived in the same way by both the assessor and the users. We
propose a new definition of relevance for XML retrieval and argue that its
corresponding relevance scale would be a better choice for INEX
Enhancing Content-And-Structure Information Retrieval using a Native XML Database
Three approaches to content-and-structure XML retrieval are analysed in this
paper: first by using Zettair, a full-text information retrieval system; second
by using eXist, a native XML database, and third by using a hybrid XML
retrieval system that uses eXist to produce the final answers from likely
relevant articles retrieved by Zettair. INEX 2003 content-and-structure topics
can be classified in two categories: the first retrieving full articles as
final answers, and the second retrieving more specific elements within articles
as final answers. We show that for both topic categories our initial hybrid
system improves the retrieval effectiveness of a native XML database. For
ranking the final answer elements, we propose and evaluate a novel retrieval
model that utilises the structural relationships between the answer elements of
a native XML database and retrieves Coherent Retrieval Elements. The final
results of our experiments show that when the XML retrieval task focusses on
highly relevant elements our hybrid XML retrieval system with the Coherent
Retrieval Elements module is 1.8 times more effective than Zettair and 3 times
more effective than eXist, and yields an effective content-and-structure XML
retrieval
Use of Wikipedia Categories in Entity Ranking
Wikipedia is a useful source of knowledge that has many applications in
language processing and knowledge representation. The Wikipedia category graph
can be compared with the class hierarchy in an ontology; it has some
characteristics in common as well as some differences. In this paper, we
present our approach for answering entity ranking queries from the Wikipedia.
In particular, we explore how to make use of Wikipedia categories to improve
entity ranking effectiveness. Our experiments show that using categories of
example entities works significantly better than using loosely defined target
categories
Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database
This paper investigates the impact of three approaches to XML retrieval:
using Zettair, a full-text information retrieval system; using eXist, a native
XML database; and using a hybrid system that takes full article answers from
Zettair and uses eXist to extract elements from those articles. For the
content-only topics, we undertake a preliminary analysis of the INEX 2003
relevance assessments in order to identify the types of highly relevant
document components. Further analysis identifies two complementary sub-cases of
relevance assessments ("General" and "Specific") and two categories of topics
("Broad" and "Narrow"). We develop a novel retrieval module that for a
content-only topic utilises the information from the resulting answer list of a
native XML database and dynamically determines the preferable units of
retrieval, which we call "Coherent Retrieval Elements". The results of our
experiments show that -- when each of the three systems is evaluated against
different retrieval scenarios (such as different cases of relevance
assessments, different topic categories and different choices of evaluation
metrics) -- the XML retrieval systems exhibit varying behaviour and the best
performance can be reached for different values of the retrieval parameters. In
the case of INEX 2003 relevance assessments for the content-only topics, our
newly developed hybrid XML retrieval system is substantially more effective
than either Zettair or eXist, and yields a robust and a very effective XML
retrieval.Comment: Postprint version. The editor version can be accessed through the DO
Entity Ranking in Wikipedia
The traditional entity extraction problem lies in the ability of extracting
named entities from plain text using natural language processing techniques and
intensive training from large document collections. Examples of named entities
include organisations, people, locations, or dates. There are many research
activities involving named entities; we are interested in entity ranking in the
field of information retrieval. In this paper, we describe our approach to
identifying and ranking entities from the INEX Wikipedia document collection.
Wikipedia offers a number of interesting features for entity identification and
ranking that we first introduce. We then describe the principles and the
architecture of our entity ranking system, and introduce our methodology for
evaluation. Our preliminary results show that the use of categories and the
link structure of Wikipedia, together with entity examples, can significantly
improve retrieval effectiveness.Comment: to appea
Maximising Social Interactions and Effectiveness within Distance Learning Courses: Cases from Construction
Advanced Internet technologies have revolutionised the delivery of distance learning education. As a result, the physical proximity between learners and the learning providers has become less important. However, whilst the pervasiveness of these technological developments has reached unprecedented levels, critics argue that the student learning experience is still not as effective as conventional face-to-face delivery. In this regard, surveys of distance learning courses reveal that there is often a lack of social interaction attributed to this method of delivery, which tends to leave learners feeling isolated due to a lack of engagement, direction, guidance and support by the tutor. This paper defines and conceptualises this phenomenon by investigating the extent to which distance-learning programmes provide the social interactions of an equivalent traditional classroom setting. In this respect, two distance learning case studies were investigated, covering the UK and Slovenian markets respectively. Research findings identified that delivery success is strongly dependent on the particular context to which the specific distance learning course is
designed, structured and augmented. It is therefore recommended that designers of distance learning courses should balance the tensions and nuances associated with commercial viability and pedagogic effectiveness
A Specification Language for Dynamic Virtual Video Sequence Generation
This paper describes the syntax and semantics of the specification language for associative chaining. The language allows the specification of entity types for association generation, the specification of conjunctions and disjunctions of entity types, initial values, fixed constraints, and weights upon entity types. A short example is presented to illustrate the use of the language and how it may be used to generate different video sequences from a common database of video components