PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

A Vector Field Approach to Lexical Semantics

We report work in progress on measuring &quot;forces&quot; underlying the semantic drift by comparing it with plate tectonics in geology. Based on a brief survey of energy as a key concept in machine learning, and the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Until evidence to the contrary, it was assumed that a classical field in physics is appropriate to model word semantics. The approach used the distributional hypothesis to statistically model word meaning. We do not address the modelling of sentence meaning here. The computability of a vector field for the indexing vocabulary of the Reuters-21578 test collection by an emergent self-organizing map suggests that energy minima as learnables in machine learning presuppose concepts as energy minima in cognition. Our finding needs to be confirmed by a systematic evaluation.Sponsorship:European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES</p

A Vector Field Approach to Lexical Semantics

We report work in progress on measuring &quot;forces&quot; underlying the semantic drift by comparing it with plate tectonics in geology. Based on a brief survey of energy as a key concept in machine learning, and the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Until evidence to the contrary, it was assumed that a classical field in physics is appropriate to model word semantics. The approach used the distributional hypothesis to statistically model word meaning. We do not address the modelling of sentence meaning here. The computability of a vector field for the indexing vocabulary of the Reuters-21578 test collection by an emergent self-organizing map suggests that energy minima as learnables in machine learning presuppose concepts as energy minima in cognition. Our finding needs to be confirmed by a systematic evaluation.Sponsorship:European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES</p

A Vector Field Approach to Lexical Semantics

We report work in progress on measuring &quot;forces&quot; underlying the semantic drift by comparing it with plate tectonics in geology. Based on a brief survey of energy as a key concept in machine learning, and the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Until evidence to the contrary, it was assumed that a classical field in physics is appropriate to model word semantics. The approach used the distributional hypothesis to statistically model word meaning. We do not address the modelling of sentence meaning here. The computability of a vector field for the indexing vocabulary of the Reuters-21578 test collection by an emergent self-organizing map suggests that energy minima as learnables in machine learning presuppose concepts as energy minima in cognition. Our finding needs to be confirmed by a systematic evaluation.Sponsorship:European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES</p