30,040 research outputs found
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextualized language model BERT for the task of ad hoc table
retrieval. We investigate how to encode table content considering the table
structure and input length limit of BERT. We also propose an approach that
incorporates features from prior literature on table retrieval and jointly
trains them with BERT. In experiments on public datasets, we show that our best
approach can outperform the previous state-of-the-art method and BERT baselines
with a large margin under different evaluation metrics.Comment: Accepted at SIGIR 2020 (Long
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
Report of MIRACLE team for the Ad-Hoc track in CLEF 2007
This paper presents the 2007 MIRACLE’s team approach to the AdHoc Information Retrieval track. The work carried out for this campaign has been reduced to monolingual experiments, in the standard and in the robust tracks. No new approaches have been attempted in this campaign, following the procedures established in our participation in previous campaigns. For this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, Hungarian, and Czech. - Robust monolingual: French, English and Portuguese. There is still some room for improvement around multilingual named entities recognition
On the use of clustering and the MeSH controlled vocabulary to improve MEDLINE abstract search
Databases of genomic documents contain substantial amounts of structured information in addition to the texts of titles and abstracts. Unstructured information retrieval techniques fail to take advantage of the structured information available. This paper describes a technique to
improve upon traditional retrieval methods by clustering the retrieval result set into two distinct clusters using additional structural information. Our hypothesis is that the relevant documents are to be found in the tightest cluster of the two, as suggested by van Rijsbergen's cluster
hypothesis. We present an experimental evaluation of these ideas based on the relevance judgments of the 2004 TREC workshop Genomics track, and the CLUTO software clustering
package
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
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