This paper describes a rule based semantic parsing approach for Kashmiri language(multilingual language) .A rule based semantic parser relies on the WORDNET and FRAMENET available for the language. Semantic parsing involves mapping of natural language (NL) sentence into a complete formal meaning representation (MR) in a meaning representation language. Semantic parsing increases the level of un...

Abstract Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations relatedMeaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based pa...

The development of larger scale natural language systems has been hampered by the need to manually create mappings from syntactic structures into meaning representations. A new approach to semantic interpretation is proposed, which uses partial syntactic structures as the main unit of abstraction for interpretation rules. This approach can work for a variety of syntactic representations corresp...

We use kernel Canonical Correlation Analysis to learn a semantic representation of Web images and their associated text. This representation is used in two applications. In first application we consider classification of images into one of three categories. We use SVM in the semantic space and compare against the SVM on raw data and against previously published results using ICA. In the second ...

The Semantic Web is fastly being developed with RDF (and RDFS) being widely accepted as core representation formalism. On the basis of RDF, a number of proposals have been made to provide a semantically richer language. Due to the many different, often conflicting requirements that knowledge representation languages deal with, there does not appear a single language on the horizon, but rather a...

The next generation of the Web is often characterized as the “Semantic Web”: information will no longer only be intended for human readers, but also for processing by machines, enabling intelligent information services, personalized Web-sites, and semantically empowered search-engines. The Semantic Web requires interoperability on the semantic level. Semantic interoperability requires standards...

We provide an integrated ontological framework offering coverage for deep semantic content, including ontological representation of multimedia based on the MPEG-7 standard. We link the deep semantic level with the media-specific semantic level to operationalize multimedia information. Through the link between multimedia representation and the semantics of specific domains we approach the Semant...

Structured link vector model (SLVM) and its improved version depend on statistical term measures to implement XML document representation. As a result, they ignore the lexical semantics of terms and its mutual information, leading to text classification errors. This paper proposed a XML document representation method, WordNet-based lexical-semantic SLVM, to solve the problem. Using WordNet, thi...

We propose a method for semantic structure analysis of noun phrases using Abstract Meaning Representation (AMR). AMR is a graph representation for the meaning of a sentence, in which noun phrases (NPs) are manually annotated with internal structure and semantic relations. We extract NPs from the AMR corpus and construct a data set of NP semantic structures. We also propose a transition-based al...

We investigate some aspects of interexpressiveness of languages and their (denotational) semantic models by viewing semantic functions from a complexity-theory viewpoint. We classify semantic functions as polynomial or nite if a language term of size n produces a metalanguage object respectively polynomially bounded in n or nite. Languages involving concurrency manifest most interest which we a...