Semantic Stability in Social Tagging Streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language streams alone

Reason Maintenance - Conceptual Framework

This paper describes the conceptual framework for reason maintenance developed as part of WP2

How to prevent type-flaw attacks on security protocols under algebraic properties

Type-flaw attacks upon security protocols wherein agents are led to misinterpret message types have been reported frequently in the literature. Preventing them is crucial for protocol security and verification. Heather et al. proved that tagging every message field with it's type prevents all type-flaw attacks under a free message algebra and perfect encryption system. In this paper, we prove that type-flaw attacks can be prevented with the same technique even under the ACUN algebraic properties of XOR which is commonly used in "real-world" protocols such as SSL 3.0. Our proof method is general and can be easily extended to other monoidal operators that possess properties such as Inverse and Idempotence as well. We also discuss how tagging could be used to prevent type-flaw attacks under other properties such as associativity of pairing, commutative encryption, prefix property and homomorphic encryption.Comment: 16 pages, Appeared in proceedings of Security with Rewriting Techniques (SecRet09), Affiliated to CSF Symposium 2009, Port Jefferson, NY

Active Learning for Dialogue Act Classification

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result

Syntactic annotation of non-canonical linguistic structures

This paper deals with the syntactic annotation of corpora that contain both ‘canonical’ and ‘non-canonical’ sentences