Dependency-based mapping between symbolic language and Extended Conceptual Graph

Kovács, László; Baksa-Varga, Erika

doi:10.1109/sisy.2008.4664911

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…For transforming the grammatical rules into CGs they use heuristics methods. Other researchers use link grammar [19,40] or dependency grammar [21]. In the latter work the authors use supervised learning to classify concepts, relations, and structures.…”

Section: Deterministic Automatic Transformationmentioning

confidence: 99%

Information Retrieval with a Simplified Conceptual Graph-Like Representation

Ordoñez-Salinas

Gelbukh

2010

Advances in Artificial Intelligence

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Abstract. We argue for that taking into account semantic relations between words in the text can improve information retrieval performance. We implemented the process of information retrieval with simplified Conceptual Graphlike structures and compare the results with those of the vector space model. Our semantic representation, combined with a small simplification of the vector space model, gives better results. In order to build Conceptual Graph-like representation, we have developed a grammar based on the dependency formalism and the standard defined for Conceptual Graphs (CG). We used noun premodifiers and noun post-modifiers, as well as verb frames, extracted from VerbNet, as a source of definition of semantic roles. VerbNet was chosen since its definitions of semantic roles have much in common with the CG standard. We experimented on a subset of the ImageClef 2008 collection of titles and annotations of medical images.

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Section: Deterministic Automatic Transformationmentioning

confidence: 99%

Information Retrieval with a Simplified Conceptual Graph-Like Representation

Ordoñez-Salinas

Gelbukh

2010

Advances in Artificial Intelligence

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show abstract

“…However a text is more than a set of words and their frequencies. Enhancing the bag-of-words approach with linguistic features has also attracted several works (e.g., [12,11,13,22], [23,16,10], [21,6]).…”

Section: Introductionmentioning

confidence: 99%