769 research outputs found
HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset
This work is a detailed companion reproducibility paper of the methods and experiments proposed by Lastra-DĂaz and GarcĂa-Serrano in (2015, 2016) [56–58], which introduces the following contributions: (1) a new and efficient representation model for taxonomies, called PosetHERep, which is an adaptation of the half-edge data structure commonly used to represent discrete manifolds and planar graphs; (2) a new Java software library called the Half-Edge Semantic Measures Library (HESML) based on PosetHERep, which implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature; (3) a set of reproducible experiments on word similarity based on HESML and ReproZip with the aim of exactly reproducing the experimental surveys in the three aforementioned works; (4) a replication framework and dataset, called WNSimRep v1, whose aim is to assist the exact replication of most methods reported in the literature; and finally, (5) a set of scalability and performance benchmarks for semantic measures libraries. PosetHERep and HESML are motivated by several drawbacks in the current semantic measures libraries, especially the performance and scalability, as well as the evaluation of new methods and the replication of most previous methods. The reproducible experiments introduced herein are encouraged by the lack of a set of large, self-contained and easily reproducible experiments with the aim of replicating and confirming previously reported results. Likewise, the WNSimRep v1 dataset is motivated by the discovery of several contradictory results and difficulties in reproducing previously reported methods and experiments. PosetHERep proposes a memory-efficient representation for taxonomies which linearly scales with the size of the taxonomy and provides an efficient implementation of most taxonomy-based algorithms used by the semantic measures and IC models, whilst HESML provides an open framework to aid research into the area by providing a simpler and more efficient software architecture than the current software libraries. Finally, we prove the outperformance of HESML on the state-of-the-art libraries, as well as the possibility of significantly improving their performance and scalability without caching using PosetHERep
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be achieved on such categories even without labeled training
data. Given image and class embeddings, we learn a compatibility function such
that matching embeddings are assigned a higher score than mismatching ones;
zero-shot classification of an image proceeds by finding the label yielding the
highest joint compatibility score. We use state-of-the-art image features and
focus on different supervised attributes and unsupervised output embeddings
either derived from hierarchies or learned from unlabeled text corpora. We
establish a substantially improved state-of-the-art on the Animals with
Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate
that purely unsupervised output embeddings (learned from Wikipedia and improved
with fine-grained text) achieve compelling results, even outperforming the
previous supervised state-of-the-art. By combining different output embeddings,
we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for
Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and
Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed
and Daniel Walter and Honglak Lee and Bernt Schiele}
The performance of text similarity algorithms
Text similarity measurement compares text with available references to indicate the degree of similarity between those objects. There have been many studies of text similarity and resulting in various approaches and algorithms. This paper investigates four majors text similarity measurements which include String-based, Corpus-based, Knowledge-based, and Hybrid similarities. The results of the investigation showed that the semantic similarity approach is more rational in finding substantial relationship between texts
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien fĂĽr die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten ĂĽberprĂĽft.
Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die veränderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt
sich mit der system-basierten Evaluation von Indizierungssystemen fĂĽr
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen fĂĽr das Suchen und Verschlagworten von
Bildern, Evaluationsmaße für die Qualitätsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und
experimentell verglichen. Traditionelle MaĂźe zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. EvaluationsmaĂźe vergeben
üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
ĂĽberprĂĽft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell fĂĽr die konzeptbasierte Suche von Bildern,
eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die
anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
Hybrid Query Expansion on Ontology Graph in Biomedical Information Retrieval
Nowadays, biomedical researchers publish thousands of papers and journals every day. Searching through biomedical literature to keep up with the state of the art is a task of increasing difficulty for many individual researchers. The continuously increasing amount of biomedical text data has resulted in high demands for an efficient and effective biomedical information retrieval (BIR) system. Though many existing information retrieval techniques can be directly applied in BIR, BIR distinguishes itself in the extensive use of biomedical terms and abbreviations which present high ambiguity. First of all, we studied a fundamental yet simpler problem of word semantic similarity. We proposed a novel semantic word similarity algorithm and related tools called Weighted Edge Similarity Tools (WEST). WEST was motivated by our discovery that humans are more sensitive to the semantic difference due to the categorization than that due to the generalization/specification. Unlike most existing methods which model the semantic similarity of words based on either the depth of their Lowest Common Ancestor (LCA) or the traversal distance of between the word pair in WordNet, WEST also considers the joint contribution of the weighted distance between two words and the weighted depth of their LCA in WordNet. Experiments show that weighted edge based word similarity method has achieved 83.5% accuracy to human judgments. Query expansion problem can be viewed as selecting top k words which have the maximum accumulated similarity to a given word set. It has been proved as an effective method in BIR and has been studied for over two decades. However, most of the previous researches focus on only one controlled vocabulary: MeSH. In addition, early studies find that applying ontology won\u27t necessarily improve searching performance. In this dissertation, we propose a novel graph based query expansion approach which is able to take advantage of the global information from multiple controlled vocabularies via building a biomedical ontology graph from selected vocabularies in Metathesaurus. We apply Personalized PageRank algorithm on the ontology graph to rank and identify top terms which are highly relevant to the original user query, yet not presented in that query. Those new terms are reordered by a weighted scheme to prioritize specialized concepts. We multiply a scaling factor to those final selected terms to prevent query drifting and append them to the original query in the search. Experiments show that our approach achieves 17.7% improvement in 11 points average precision and recall value against Lucene\u27s default indexing and searching strategy and by 24.8% better against all the other strategies on average. Furthermore, we observe that expanding with specialized concepts rather than generalized concepts can substantially improve the recall-precision performance. Furthermore, we have successfully applied WEST from the underlying WordNet graph to biomedical ontology graph constructed by multiple controlled vocabularies in Metathesaurus. Experiments indicate that WEST further improve the recall-precision performance. Finally, we have developed a Graph-based Biomedical Search Engine (G-Bean) for retrieving and visualizing information from literature using our proposed query expansion algorithm. G-Bean accepts any medical related user query and processes them with expanded medical query to search for the MEDLINE database
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity
Modelling semantic similarity plays a fundamental role in lexical semantic
applications. A natural way of calculating semantic similarity is to access
handcrafted semantic networks, but similarity prediction can also be
anticipated in a distributional vector space. Similarity calculation continues
to be a challenging task, even with the latest breakthroughs in deep neural
language models. We first examined popular methodologies in measuring taxonomic
similarity, including edge-counting that solely employs semantic relations in a
taxonomy, as well as the complex methods that estimate concept specificity. We
further extrapolated three weighting factors in modelling taxonomic similarity.
To study the distinct mechanisms between taxonomic and distributional
similarity measures, we ran head-to-head comparisons of each measure with human
similarity judgements from the perspectives of word frequency, polysemy degree
and similarity intensity. Our findings suggest that without fine-tuning the
uniform distance, taxonomic similarity measures can depend on the shortest path
length as a prime factor to predict semantic similarity; in contrast to
distributional semantics, edge-counting is free from sense distribution bias in
use and can measure word similarity both literally and metaphorically; the
synergy of retrofitting neural embeddings with concept relations in similarity
prediction may indicate a new trend to leverage knowledge bases on transfer
learning. It appears that a large gap still exists on computing semantic
similarity among different ranges of word frequency, polysemous degree and
similarity intensity
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien fĂĽr die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten ĂĽberprĂĽft.
Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die veränderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt
sich mit der system-basierten Evaluation von Indizierungssystemen fĂĽr
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen fĂĽr das Suchen und Verschlagworten von
Bildern, Evaluationsmaße für die Qualitätsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und
experimentell verglichen. Traditionelle MaĂźe zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. EvaluationsmaĂźe vergeben
üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
ĂĽberprĂĽft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell fĂĽr die konzeptbasierte Suche von Bildern,
eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die
anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
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