97 research outputs found
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
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
Queensland University of Technology at TREC 2005
The Information Retrieval and Web Intelligence (IR-WI) research group is a research team at the Faculty of Information Technology, QUT, Brisbane, Australia. The IR-WI group participated in the Terabyte and Robust track at TREC 2005, both for the first time. For the Robust track we applied our existing information retrieval system that was originally designed for use with structured (XML) retrieval to the domain of document retrieval. For the Terabyte track we experimented with an open source IR system, Zettair and performed two types of experiments. First, we compared Zettair’s performance on both a high-powered supercomputer and a distributed system across seven midrange personal computers. Second, we compared Zettair’s performance when a standard TREC title is used, compared with a natural language query, and a query expanded with synonyms. We compare the systems both in terms of efficiency and retrieval performance. Our results indicate that the distributed system is faster than the supercomputer, while slightly decreasing retrieval performance, and that natural language queries also slightly decrease retrieval performance, while our query expansion technique significantly decreased performance
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
TRECVid 2006 experiments at Dublin City University
In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2006. We submitted the following six automatic runs:
• F A 1 DCU-Base 6: Baseline run using only ASR/MT text features.
• F A 2 DCU-TextVisual 2: Run using text and visual features.
• F A 2 DCU-TextVisMotion 5: Run using text, visual, and motion features.
• F B 2 DCU-Visual-LSCOM 3: Text and visual features combined with concept detectors.
• F B 2 DCU-LSCOM-Filters 4: Text, visual, and motion features with concept detectors.
• F B 2 DCU-LSCOM-2 1: Text, visual, motion, and concept detectors with negative concepts.
The experiments were designed both to study the addition of motion features and separately constructed models for semantic concepts, to runs using only textual and visual features, as well as to establish a baseline for the manually-assisted search runs performed within the collaborative K-Space project and described in the corresponding TRECVid 2006 notebook paper. The results of
the experiments indicate that the performance of automatic search can be improved with suitable concept models. This, however, is very topic-dependent and the questions of when to include such models and which concept models should be included, remain unanswered. Secondly, using motion features did not lead to performance improvement in our experiments. Finally, it was observed that our text features, despite displaying a rather poor performance overall, may still be useful even for generic search topics
Fuzzy term proximity with boolean queries at 2006 TREC Terabyte task
http://trec.nist.gov/pubs/trec15/papers/ecole.tera.final.pdfInternational audienceWe report here the results of fuzzy term proximity method app lied to Terabyte Task. Fuzzy proxmity main feature is based on the idea that the clos er the query terms are in a document, the more relevant this document is. With this p rinciple, we have a high precision method so we complete by these obtained with Zettair search engine default method (dirichlet). Our model is able to deal with Boolean qu eries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to node s. The fuzzy term proximity is controlled with an influence function. Given a query term a nd a document, the influence function associates to each position in the text a value depe ndant of the distance of the nearest occurence of this query term. To model proximity, th is function is decreasing with distance. Different forms of function can be used: triangula r, gaussian etc. For practical reasons only functions with finite support were used. The sup port of the function is limited by a constant called k. The fuzzy term proximity func tions are associated to every leaves of the query tree. Then fuzzy proximities are co mputed for every nodes with a post-order tree traversal. Given the fuzzy proximities of the sons of a node, its fuzzy proximity is computed, like in the fuzzy IR models, with a mim imum (resp. maximum) combination for conjunctives (resp. disjunctives) nodes. Finally, a fuzzy query proximity value is obtained for each position in this document at the ro ot of the query tree. The score of this document is the integration of the function obt ained at the tree root. For the experiments, we modify Lucy (version 0.5.2) to implement ou r matching function. Two query sets are used for our runs. One set is manually built wit h the title words (and sometimes some description words). Each of these words is OR 'ed with its derivatives like plurals for instance. Then the OR nodes obtained are AND'ed a t the tree root. An other automatic query sets is built with an AND of automatically ex tracted terms from the title field. These two query sets are submitted to our system with tw o values of k: 50 and 200. The two corresponding query sets with flat queries are also su bmitted to zettair search engine
Structure et proximité pour la recherche documentaire
http://asso-aria.org/coria/2009/373.pdfInternational audienceNotre étude compare les performances d'un système de recherche d'information basé sur la proximité des occurrences des termes de la requête dans les documents avec un système classique de modèle de langue avec lissage de Dirichlet et le modèle Okapi BM25 . Notre modèle basé sur la proximité calcule en chaque position du document une valeur d'autant plus grande que des occurrences de tous les termes de la requête sont proches de cette position. De plus pour le modèle à proximité nous testons dans le cas de documents structurés l'hypothèse que les termes apparaissant dans les titres doivent être considérés comme proches des positions de toute la section correspondant à ce titre
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