scholarly journals Analyzing Chat Conversations of Pedophiles with Temporal Relational Semantic Systems

2012 ◽  
Author(s):  
Paul Elzinga ◽  
Karl Erich Wolff ◽  
Jonas Poelmans
Keyword(s):  
Relational Semantic

scholarly journals A Study on Relational Semantic Video Content Extraction Using Bayesian Network Classifier

2014 ◽  
Vol 16 (3) ◽  
pp. 106-109
Author(s):  
S Rajarajeshwari ◽  
◽  
G.Kalaimathi Priya ◽  
S.Grace Mary ◽  
Dr.V.Sambath kumar
Keyword(s):  
Bayesian Network ◽  
Video Content ◽  
Content Extraction ◽  
Relational Semantic ◽  
Bayesian Network Classifier ◽  
Video Content Extraction

Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

2016 ◽  
Vol 10 (3) ◽  
pp. 13-28 ◽  
Author(s):  
Yingxu Wang ◽  
Mehrdad Valipour ◽  
Omar A. Zatarain
Keyword(s):  
Machine Learning ◽  
Semantic Analysis ◽  
Semantic Space ◽  
Semantic Equivalence ◽  
Semantic Classification ◽  
Relational Semantic ◽  
Semantic Hierarchy ◽  
Formal Concepts ◽  
Concept Classification ◽  
The Brain

Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.

scholarly journals OntoNotes: A Unified Relational Semantic Representation

2007 ◽  
Author(s):  
Sameer S. Pradhan ◽  
Eduard Hovy ◽  
Mitch Marcus ◽  
Martha Palmer ◽  
Lance Ramshaw ◽  
...  
Keyword(s):  
Semantic Representation ◽  
Relational Semantic

Distributed Spatial Semantics

1995 ◽  
Vol 18 (2) ◽  
pp. 167-199 ◽  
Author(s):  
Chris Sinha ◽  
Tania Kuteva
Keyword(s):  
Semantic Information ◽  
Relational Semantics ◽  
Linguistic Expression ◽  
Spatial Prepositions ◽  
Relational Semantic ◽  
Selectional Restrictions ◽  
Distributed Approach ◽  
Change Of Perspective ◽  
Semantic Types ◽  
Spatial Semantics

The “local semantics” approach to the analysis of the meaning of locative particles (e.g. spatial prepositions) is examined, criticized and rejected. An alternative, distributed approach to spatial relational semantics and its linguistic expression is argued for. In the first part of the paper, it is argued that spatial relational semantic information is not exclusively carried in languages such as English by the locative particle, and that “item-specific meanings plus selectional restrictions” cannot save the localist approach. In the second part of the paper, the “covertly” distributed spatial relational semantics of languages such as English is contrasted with the “overtly” distributed spatial relational semantics characterizing many other languages. Some common assumptions relating to the universality of the expression of spatial relational meaning by closed syntactic classes are criticized. A change of perspective from “local” to “distributed” semantics permits the re-analysis of polysemy and item-bound “use-type” in terms of the distributed expression of language-specific spatial relational semantic types.

scholarly journals ONTONOTES: A UNIFIED RELATIONAL SEMANTIC REPRESENTATION

2007 ◽  
Vol 01 (04) ◽  
pp. 405-419 ◽  
Author(s):  
SAMEER S. PRADHAN ◽  
EDUARD HOVY ◽  
MITCH MARCUS ◽  
MARTHA PALMER ◽  
LANCE RAMSHAW ◽  
...  
Keyword(s):  
Relational Database ◽  
Large Scale ◽  
Semantic Analysis ◽  
Semantic Representation ◽  
Object Oriented ◽  
Semantic Structure ◽  
Relational Semantic ◽  
Multiple Levels

The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.

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