74 research outputs found
Search of spoken documents retrieves well recognized transcripts
This paper presents a series of analyses and experiments on spoken
document retrieval systems: search engines that retrieve transcripts produced by
speech recognizers. Results show that transcripts that match queries well tend to
be recognized more accurately than transcripts that match a query less well.
This result was described in past literature, however, no study or explanation of
the effect has been provided until now. This paper provides such an analysis
showing a relationship between word error rate and query length. The paper
expands on past research by increasing the number of recognitions systems that
are tested as well as showing the effect in an operational speech retrieval
system. Potential future lines of enquiry are also described
Streamlined Data Fusion: Unleashing the Power of Linear Combination with Minimal Relevance Judgments
Linear combination is a potent data fusion method in information retrieval
tasks, thanks to its ability to adjust weights for diverse scenarios. However,
achieving optimal weight training has traditionally required manual relevance
judgments on a large percentage of documents, a labor-intensive and expensive
process. In this study, we investigate the feasibility of obtaining
near-optimal weights using a mere 20\%-50\% of relevant documents. Through
experiments on four TREC datasets, we find that weights trained with multiple
linear regression using this reduced set closely rival those obtained with
TREC's official "qrels." Our findings unlock the potential for more efficient
and affordable data fusion, empowering researchers and practitioners to reap
its full benefits with significantly less effort.Comment: 12 pages, 8 figure
Explainable Information Retrieval: A Survey
Explainable information retrieval is an emerging research area aiming to make
transparent and trustworthy information retrieval systems. Given the increasing
use of complex machine learning models in search systems, explainability is
essential in building and auditing responsible information retrieval models.
This survey fills a vital gap in the otherwise topically diverse literature of
explainable information retrieval. It categorizes and discusses recent
explainability methods developed for different application domains in
information retrieval, providing a common framework and unifying perspectives.
In addition, it reflects on the common concern of evaluating explanations and
highlights open challenges and opportunities.Comment: 35 pages, 10 figures. Under revie
Learning to Choose : automatic Selection of the Information Retrieval Parameters
International audienceIn this paper we promote a selective information retrieval process to be applied in the context of repeated queries. The method is based on a training phase in which the meta search system learns the best parameters to use on a per query basis. The training phase uses a sample of annotated documents for which document relevance is known. When an equal-query is submitted to the system, it automatically knows which parameters it should use to treat the query. This Learning to choose method is evaluated using simulated data from TREC campaigns. We show that system performance highly increases in terms of precision (MAP), speci cally for the queries that are di cult to answer, when compared to any unique system con guration applied to all the queries
Examining repetition in user search behavior
This paper describes analyses of the repeated use of search engines.
It is shown that users commonly re-issue queries, either to examine search
results deeply or simply to query again, often days or weeks later. Hourly and
weekly periodicities in behavior are observed for both queries and clicks.
Navigational queries were found to be repeated differently from others
LESIM: A Novel Lexical Similarity Measure Technique for Multimedia Information Retrieval
Metadata-based similarity measurement is far from obsolete in our days, despite research’s focus on content and context. It allows for aggregating information from textual references, measuring similarity when content is not available, traditional keyword search in search engines, merging results in meta-search engines and many more research and industry interesting activities. Existing similarity measures do not take into consideration neither the unique nature of multimedia’s metadata nor the requirements of metadata-based information retrieval of multimedia. This work proposes a customised for the commonly available author-title multimedia metadata hybrid similarity measure that is shown through experimentation to be significantly more effective than baseline measures
A multi-collection latent topic model for federated search
Collection selection is a crucial function, central to the effectiveness and efficiency of a federated information retrieval system. A variety of solutions have been proposed for collection selection adapting proven techniques used in centralised retrieval. This paper defines a new approach to collection selection that models the topical distribution in each collection. We describe an extended version of latent Dirichletallocation that uses a hierarchical hyperprior to enable the different topical distributions found in each collection to be modelled. Under the model, resources are ranked based on the topical relationship between query and collection. By modelling collections in a low dimensional topic space, we can implicitly smooth their term-based characterisation with appropriate terms from topically related samples, thereby dealing with the problem of missing vocabulary within the samples. An important advantage of adopting this hierarchical model over current approaches is that the model generalises well to unseen documents given small samples of each collection. The latent structure of each collection can therefore be estimated well despite imperfect information for each collection such as sampled documents obtained through query-based sampling. Experiments demonstrate that this new, fully integrated topical model is more robust than current state of the art collection selection algorithm
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Training high-quality instance segmentation models requires an abundance of
labeled images with instance masks and classifications, which is often
expensive to procure. Active learning addresses this challenge by striving for
optimum performance with minimal labeling cost by selecting the most
informative and representative images for labeling. Despite its potential,
active learning has been less explored in instance segmentation compared to
other tasks like image classification, which require less labeling. In this
study, we propose a post-hoc active learning algorithm that integrates
uncertainty-based sampling with diversity-based sampling. Our proposed
algorithm is not only simple and easy to implement, but it also delivers
superior performance on various datasets. Its practical application is
demonstrated on a real-world overhead imagery dataset, where it increases the
labeling efficiency fivefold.Comment: UNCV ICCV 202
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
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