39,183 research outputs found
Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets
A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g. gathered from a thorough log file analysis, can be used to train classifiers which aim to inference relevance of retrieved documents based on user interactions. A problem in this approach is, however, the large imbalance ratio between relevant and non-relevant documents in the collection. In standard test collection as used in academic evaluation frameworks such as TREC, non-relevant documents outnumber relevant documents by far. In this work, we address this imbalance problem in the multimedia domain. We focus on the logs of two multimedia user studies which are highly imbalanced. We compare a naiinodotve solution of randomly deleting documents belonging to the majority class with various balancing algorithms coming from different fields: data classification and text classification. Our experiments indicate that all algorithms improve the classification performance of just deleting at random from the dominant class
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the
imbalanced classes in the data set. Too few clicked documents may hurt
generalization ability of the models, whereas too many non-clicked documents
may harm effectiveness of the models and efficiency of training. In addition,
recent neural network-based models are vulnerable to adversarial examples due
to the linear nature in them. To solve the problems at the same time, we
propose an adversarial sampling and training framework to learn ad-hoc
retrieval models with implicit feedback. Our key idea is (i) to augment clicked
examples by adversarial training for better generalization and (ii) to obtain
very informational non-clicked examples by adversarial sampling and training.
Experiments are performed on benchmark data sets for common ad-hoc retrieval
tasks such as Web search, item recommendation, and question answering.
Experimental results indicate that the proposed approaches significantly
outperform strong baselines especially for high-ranked documents, and they
outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search
task.Comment: Published in WWW 201
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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
Automatic tagging and geotagging in video collections and communities
Automatically generated tags and geotags hold great promise
to improve access to video collections and online communi-
ties. We overview three tasks offered in the MediaEval 2010
benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
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