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84
WordNet Ontology Based Model for Web Retrieval
- Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration, Tokyo,Japan
"... Abstract—It is well known that ontologies will become a key piece, as they allow making the semantics of Semantic Web content [16] explicit. In spite of the big advantages that the Semantic Web promises, there are still several problems to solve. Those concerning ontologies include their availabilit ..."
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Cited by 6 (1 self)
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exact words when looking for a document, thus we miss some relevant documents. LSI is a numerical method, which discovers latent semantics in documents by creating concepts from existing terms. In this paper we present a basic method of mapping LSI concepts on given ontology (WordNet), used both
Knowledge and Content-based Audio Retrieval using WordNet Pedro Cano and Markus Koppenberger and Sylvain Le Groux
, 2004
"... Sound producers create the sound that goes along the image in cinema and video productions, as well as spots and documentaries. Some sounds are recorded for the occasion. Many occasions, however, require the engineer to have access to massive libraries of music and sound e#ects. Of the three majo ..."
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needed in a production-size sound e#ect management system. To address this problem we use WordNet, an ontology that organizes over 100.000 concepts of real world knowledge: e.g: it relates doors to locks, to wood and to the actions of opening, closing or knocking. However a fundamental issue remains
Desiderata for Tagging with WordNet Synsets or MCCA Categories
, 1997
"... Minnesota Contextual Content Analysis (MCCA) is a technique for characterizing the concepts and themes occurring in text (sentences, paragraphs, interview transcripts, books). MCCA gs each word with a category and examines the distribution of categories against norms representing general usage of ca ..."
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Cited by 1 (0 self)
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of categories. MCCA also scores texts in terms of social contexts that are similar to different functions of language. Distributions can be analyzed using non-agglomerative clustering to characterize the concepts and themes. MCCA categories have been mapped to WordNet senses. The defining characteristics
Using BFA with WordNet Ontology Based Model for Web Retrieval
"... In the area of information retrieval, the dimension of document vectors plays an important role. We may need to find a few words or concepts, which characterize the docu-ment based on its contents, to overcome the problem of the "curse of dimensionality", which makes indexing of high-dimen ..."
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In the area of information retrieval, the dimension of document vectors plays an important role. We may need to find a few words or concepts, which characterize the docu-ment based on its contents, to overcome the problem of the "curse of dimensionality", which makes indexing of high
Automatic Spell Correction of User query with Semantic Information Retrieval and Ranking of Search Results using WordNet Approach
"... The proposed semantic information retrieval system handles the following: i) automatic spell correction of user query. ii) analysis and determination of the semantic feature of the content and development of a semantic pattern that represents the semantic features of the content. iii) analysis of us ..."
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The proposed semantic information retrieval system handles the following: i) automatic spell correction of user query. ii) analysis and determination of the semantic feature of the content and development of a semantic pattern that represents the semantic features of the content. iii) analysis
Metadata Generation for the Web Documents using WordNet Ontology
"... Abstract-The objective of this paper is to generate metadata for the web documents. Metadata plays a key role in the indexing of web documents in information retrieval systems. Metadata is generated by finding the dominant concepts of the documents, which are obtained by the using the semantic simi ..."
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similarity measures(Resnik, Lin, and Jiang & Conrath). This metadata could aid better retrieval effectiveness as semantically identified dominant concepts could better contribute to relevance than keywords. In the proposed method corpus independent information content measure is used along with the WordNet
Lexical Selection for Cross-Language Applications: Combining LCS with WordNet
- In Proceedings of AMTA’1998, Langhorne
, 1998
"... . This paper describes experiments for testing the power of large-scale resources for lexical selection in machine translation (MT) and cross-language information retrieval (CLIR). We adopt the view that verbs with similar argument structure share certain meaning components, but that those meani ..."
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Cited by 12 (3 self)
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our implementation of an algorithm for cross-language selection of lexical items, exploiting the strengths of each resource: LCS for semantic structure and WordNet for semantic content. We use the Parka Knowledge-Based System to encode LCS representations and WordNet synonym sets and we implement
Nearest-neighbor Automatic Sound Annotation with a WordNet Taxonomy
, 2004
"... Sound engineers need to access vast collections of sound effects for their film and video productions. Sound effects providers rely on text-retrieval techniques to offer their collections. Currently, annotation of audio content is done manually, which is an arduous task. Automatic annotation methods ..."
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Cited by 5 (2 self)
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of classifiers, each sp ecialized in distinguishing little details. We report experimental results on a general sound annotator. To tackle the taxonomy definition problem we use WordNet, a semantic network that organizes real world knowledge. In order to overcome the need of a huge number of classifiers
Adding semantics to detectors for video retrieval
- IEEE Transactions on Multimedia
, 2007
"... Abstract — In this paper, we propose an automatic video retrieval method based on high-level concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. Such a set of detectors is very small ..."
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Cited by 77 (14 self)
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still compared to ontologies aiming to capture the full vocabulary a user has. We aim to throw a bridge between the two fields by building a multimedia thesaurus, i.e. a set of machine learned concept detectors that is enriched with semantic descriptions and semantic structure obtained from WordNet
Semantically relevant image retrieval by combining image and linguistic analysis
- In: ISVC06
, 2006
"... Abstract. In this paper, we introduce a novel approach to image-based infor-mation retrieval by combining image analysis with linguistic analysis of associ-ated annotation information. While numerous Content Based Image Retrieval (CBIR) systems exist, most of them are constrained to use images as th ..."
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Cited by 4 (0 self)
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and annotation level, if it detects a strong semantic coherence between them. Otherwise, depending on the quality of information available, either of the media is selected to execute the search. Semantic similarity is defined through the use of linguistic relation-ships in WordNet as well as through shape
Results 1 - 10
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84
