104,147 research outputs found
Evaluating Scoped Meaning Representations
Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and presupposition triggers. The semantic formalism is based on
Discourse Representation Theory, but concepts are represented by WordNet
synsets and thematic roles by VerbNet relations. Translating scoped meaning
representations to sets of clauses enables us to compare them for the purpose
of semantic parser evaluation and checking translations. This is done by
computing precision and recall on matching clauses, in a similar way as is done
for Abstract Meaning Representations. We show that our matching tool for
evaluating scoped meaning representations is both accurate and efficient.
Applying this matching tool to three baseline semantic parsers yields F-scores
between 43% and 54%. A pilot study is performed to automatically find changes
in meaning by comparing meaning representations of translations. This
comparison turns out to be an additional way of (i) finding annotation mistakes
and (ii) finding instances where our semantic analysis needs to be improved.Comment: Camera-ready for LREC 201
A Semantic Framework for Priority-based Service Matching in Pervasive Environments
The increasing popularity of personal wireless devices has raised new demands for the efficient discovery of heterogeneous devices and services in pervasive environments. The existing approaches such as Jini [1], UPnP [8], etc., describe services at a syntactic level and the matching mechanisms in these approaches are limited to syntactic comparisons based on attributes or interfaces. In order to overcome the limitations in these approaches, there has been an increased interest in the use of semantic description and matching techniques to support effective service discovery. This paper proposes a semantic matching approach which facilitates the discovery of device-based services in a pervasive environment; the approach provides a ranking facility that orders services according to their suitability and also considers priorities placed on individual requirements in a request during the matching process. The evaluation studies have shown that the matcher results correlate reasonably well with human judgement
A Pragmatic Approach for the Semantic Description and Matching of Pervasive Resources
The increasing popularity of personal wireless devices has raised new demands for the efficient discovery of heterogeneous devices and services in pervasive environments. With the advancement of the electronic world, the diversity of available services is increasing rapidly. %This raises new demands for the efficient discovery and location of heterogeneous services and resources in dynamically changing environments. Traditional approaches for service discovery describe services at a syntactic level and the matching mechanisms available for these approaches are limited to syntactic comparisons based on attributes or interfaces. In order to overcome these limitations, there has been an increased interest in the use of semantic description and matching techniques to support effective service discovery. In this paper, we present a semantic matching approach to facilitate the discovery of device-based services in pervasive environments. The approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request during the matching process. The solution has been systematically evaluated for its retrieval effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. Another important practical concern is the efficiency and the scalability of the semantic matching solution. Therefore, we have evaluated the scalability of the proposed solution by investigating the variation in matching time in response to increasing numbers of advertisements and increasing request sizes, and have presented the empirical results
SenseCam image localisation using hierarchical SURF trees
The SenseCam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day.
Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing
semantic information with each photo, such as the wearer's location during capture time. We propose a method for automatically determining the wearer's location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further
A semantic feature for human motion retrieval
With the explosive growth of motion capture data, it becomes very imperative in animation production to have an efficient search engine to retrieve motions from large motion repository. However, because of the high dimension of data space and complexity of matching methods, most of the existing approaches cannot return the result in real time. This paper proposes a high level semantic feature in a low dimensional space to represent the essential characteristic of different motion classes. On the basis of the statistic training of Gauss Mixture Model, this feature can effectively achieve motion matching on both global clip level and local frame level. Experiment results show that our approach can retrieve similar motions with rankings from large motion database in real-time and also can make motion annotation automatically on the fly. Copyright Ā© 2013 John Wiley & Sons, Ltd
Deep Structured Features for Semantic Segmentation
We propose a highly structured neural network architecture for semantic
segmentation with an extremely small model size, suitable for low-power
embedded and mobile platforms. Specifically, our architecture combines i) a
Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random
layer realizing a radial basis function kernel approximation, and iii) a linear
classifier. While stages i) and ii) are completely pre-specified, only the
linear classifier is learned from data. We apply the proposed architecture to
outdoor scene and aerial image semantic segmentation and show that the accuracy
of our architecture is competitive with conventional pixel classification CNNs.
Furthermore, we demonstrate that the proposed architecture is data efficient in
the sense of matching the accuracy of pixel classification CNNs when trained on
a much smaller data set.Comment: EUSIPCO 2017, 5 pages, 2 figure
KOIOS: Top-k Semantic Overlap Set Search
We study the top-k set similarity search problem using semantic overlap.
While vanilla overlap requires exact matches between set elements, semantic
overlap allows elements that are syntactically different but semantically
related to increase the overlap. The semantic overlap is the maximum matching
score of a bipartite graph, where an edge weight between two set elements is
defined by a user-defined similarity function, e.g., cosine similarity between
embeddings. Common techniques like token indexes fail for semantic search since
similar elements may be unrelated at the character level. Further, verifying
candidates is expensive (cubic versus linear for syntactic overlap), calling
for highly selective filters. We propose KOIOS, the first exact and efficient
algorithm for semantic overlap search. KOIOS leverages sophisticated filters to
minimize the number of required graph-matching calculations. Our experiments
show that for medium to large sets less than 5% of the candidate sets need
verification, and more than half of those sets are further pruned without
requiring the expensive graph matching. We show the efficiency of our algorithm
on four real datasets and demonstrate the improved result quality of semantic
over vanilla set similarity search
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