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Applying Deep Belief Networks to Word Sense Disambiguation
"... In this paper, we applied a novel learn-ing algorithm, namely, Deep Belief Net-works (DBN) to word sense disambigua-tion (WSD). DBN is a probabilistic gen-erative model composed of multiple lay-ers of hidden units. DBN uses Re-stricted Boltzmann Machine (RBM) to greedily train layer by layer as a pr ..."
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In this paper, we applied a novel learn-ing algorithm, namely, Deep Belief Net-works (DBN) to word sense disambigua-tion (WSD). DBN is a probabilistic gen-erative model composed of multiple lay-ers of hidden units. DBN uses Re-stricted Boltzmann Machine (RBM) to greedily train layer by layer as a
Word Sense Disambiguation and Semantic Disambiguation for Construction Types in Deep Processing Grammars
"... The paper presents advances in the use of semantic features and interlingua relations for word sense disambiguation (WSD) as part of unification-based deep processing grammars. Formally we present an extension of Minimal Recursion Semantics, introducing sortal specifications as well as linterlingua ..."
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The paper presents advances in the use of semantic features and interlingua relations for word sense disambiguation (WSD) as part of unification-based deep processing grammars. Formally we present an extension of Minimal Recursion Semantics, introducing sortal specifications as well as linterlingua
Statistical Models for Deep-structure Disambiguation
, 1996
"... In this paper, an integrated score function is proposed to resolve the ambiguity of deepstructure, which includes the cases of constituents and the senses of words. With the integrated score function, different knowledge sources, including part-of-speech, syntax and semantics, are integrated in a un ..."
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Cited by 1 (1 self)
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In this paper, an integrated score function is proposed to resolve the ambiguity of deepstructure, which includes the cases of constituents and the senses of words. With the integrated score function, different knowledge sources, including part-of-speech, syntax and semantics, are integrated in a
Benzineb K. Analysis of Word Sense Disambiguation-Based Information Retrieval
- In 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers. C. Peters, et al. (Eds.). Lecture Notes in Computer Science (LNCS) 5706
, 2009
"... Several studies have tried to improve retrieval performances based on automatic Word Sense Disambiguation techniques. So far, most attempts have failed. We try, through this paper, to give a deep analysis of the reasons behind these failures. During our participation at the Robust WSD task at CLEF 2 ..."
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Cited by 2 (1 self)
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Several studies have tried to improve retrieval performances based on automatic Word Sense Disambiguation techniques. So far, most attempts have failed. We try, through this paper, to give a deep analysis of the reasons behind these failures. During our participation at the Robust WSD task at CLEF
Integrating visual and semantic contexts for topic network generation and word sense disambiguation
- In CIVR
, 2009
"... To support more effective searches in large-scale weakly-tagged image collections, we have developed a novel algo-rithm to integrate both the visual similarity contexts be-tween the images and the semantic similarity contexts be-tween their tags for topic network generation and word sense disambigua ..."
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Cited by 4 (1 self)
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network can make the semantics behind the tag space more explicit, so that users can gain deep insights rapidly and formulate their queries more precisely. Second, our word sense disambiguation algorithm can integrate the topic network to exploit both the visual similarity contexts between the images
DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation
"... We introduce an approach to word sense dis-ambiguation and entity linking that combines a set of complementary objectives in an exten-sible multi-objective formalism. During dis-ambiguation the system performs continuous optimization to find optimal probability dis-tributions over candidate senses. ..."
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We introduce an approach to word sense dis-ambiguation and entity linking that combines a set of complementary objectives in an exten-sible multi-objective formalism. During dis-ambiguation the system performs continuous optimization to find optimal probability dis-tributions over candidate senses
M.: Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation. Journal of Language Resources and Evaluation [under review] (2008b
"... Abstract. 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 benefi ..."
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Cited by 1 (1 self)
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Abstract. 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
Knowledge Representation and Sense Disambiguation for Interrogatives in E-HowNet
, 2008
"... In order to train machines to ‘understand ’ natural language, we propose a meaning representation mechanism called E-HowNet to encode lexical senses. In this paper, we take interrogatives as examples to demonstrate the mechanisms of semantic representation and composition of interrogative constructi ..."
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In order to train machines to ‘understand ’ natural language, we propose a meaning representation mechanism called E-HowNet to encode lexical senses. In this paper, we take interrogatives as examples to demonstrate the mechanisms of semantic representation and composition of interrogative
Highlighting relevant concepts from Topic Signatures
"... This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appro-priate WordNet sense to large sets of to ..."
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This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appro-priate WordNet sense to large sets
S.: LODifier: Generating Linked Data from Unstructured Text
- In: Proceedings of ESWC
, 2012
"... Abstract. The automated extraction of information from text and its transforma-tion into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering ..."
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Cited by 14 (3 self)
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answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF
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