73,443 research outputs found
On nonprimary selectional restrictions
This paper argues for non-primary c- and s-selectional restrictions of verbs in computing nonprimary predicatives such as resultatives, depictives, and manners. Our discussion is based both on the selection violations in the presence of nonprimary predicates and on the cross-linguistic and language-internal variations of categorial and semantic constraints on nonprimary predicates. We claim that all types of thematic predication are represented by an extended projection, and that the merger of lexical heads with another element, regardless of the type of the element, consistently has c- and s-selectional restrictions
Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation
Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources
The application of user log for online business environment using content-based Image retrieval system
Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers
Korean to English Translation Using Synchronous TAGs
It is often argued that accurate machine translation requires reference to
contextual knowledge for the correct treatment of linguistic phenomena such as
dropped arguments and accurate lexical selection. One of the historical
arguments in favor of the interlingua approach has been that, since it revolves
around a deep semantic representation, it is better able to handle the types of
linguistic phenomena that are seen as requiring a knowledge-based approach. In
this paper we present an alternative approach, exemplified by a prototype
system for machine translation of English and Korean which is implemented in
Synchronous TAGs. This approach is essentially transfer based, and uses
semantic feature unification for accurate lexical selection of polysemous
verbs. The same semantic features, when combined with a discourse model which
stores previously mentioned entities, can also be used for the recovery of
topicalized arguments. In this paper we concentrate on the translation of
Korean to English.Comment: ps file. 8 page
Translating English non-human subjects in agentive contexts : a closer look at Dutch
While subjects of transitive action verbs in English and Dutch are typically realized as human agents (see Comrie 1989), both languages also feature instances of nonhuman agents in subject position. However, Vandepitte and Hartsuiker (2011) have shown that there are fewer options in Dutch and that translation issues present themselves in cases where both languages do not overlap. This paper wants to document overlap and differences in terms of non-prototypical subject realization by focussing on the strategies that are used in Dutch translations of six actions verbs (give, demonstrate, show, suggest, offer and tell) in combination with non-human subjects. Results reveal that a fair share of non-human subjects are also translated as such in the target language. Other strategies include occasional humanization of the non-human source text subjects, reduction of valency patterns with reduced agentivity vis-a-vis the English source-text sentences and shifts in the mapping of semantic roles onto syntactic functions
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
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Hierarchical video summarisation in reference frame subspace
In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently
Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007
This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p
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