@inproceedings{Malakasiotis2007,
title = {Learning Textual Entailment using SVMs and String Similarity Measures},
author = {Prodromos Malakasiotis and Ion Androutsopoulos},
url = {http://www.aueb.gr/users/ion/docs/rte3_paper.pdf},
year = {2007},
date = {2007-06-01},
booktitle = {Proceedings of the Workshop on Textual Entailment and Paraphrasing, 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007)},
pages = {42-47},
abstract = {We present the system that we submitted to the 3rd Pascal Recognizing Textual Entailment Challenge. It uses four Support Vector Machines, one for each subtask of the challenge, with features that correspond to string similarity measures operating at the lexical and shallow syntactic level.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{Lourdi2007,
title = {A multi-layer metadata schema for digital folklore collections},
author = {Irene Lourdi and Christos Papatheodorou and Mara Nikolaidou},
url = {http://jis.sagepub.com/cgi/content/abstract/33/2/197},
year = {2007},
date = {2007-01-02},
journal = {Journal of Information Science},
volume = {33},
number = {2},
pages = {197-213},
abstract = {Digital folklore collections are valuable sources for studying the cultural and oral tradition of a country. The main difficulty in managing such collections is material heterogeneity (handwritten texts, photographs, 3D objects, sound recordings etc.) that imposes different digitization, description and maintenance practices. A multi-layer metadata model for the description of a digital folklore collection is presented. The proposed meta-data policy considers a collection as a hierarchy of entities and combines different metadata schemas for the management of each entity. The metadata model integrates elements from different metadata schemas ensuring efficient information recovery from all structural levels. Furthermore, interoperability between the used metadata schemas is discussed and a Topic Maps model is presented as an approach for developing mappings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Lucarreli2007,
title = {Named Entity Recognition in Greek Texts with an Ensemble of SVMs and Active Learning},
author = {Giorgios Lucarreli and Xenofon Vasilakos and Ion Androutsopoulos},
url = {http://db0.worldscinet.com/worldsci-staging/detail.nsp},
year = {2007},
date = {2007-01-01},
journal = {International Journal on Artificial Intelligence Tools (IJAIT)},
volume = {16},
number = {6},
pages = {1015 - 1045},
abstract = {We present a freely available named-entity recognizer for Greek texts that identifies temporal expressions, person, and organization names. For temporal expressions, it relies on semi-automatically produced patterns. For person and organization names, it employs an ensemble of Support Vector Machines that scan the input text in two passes. The ensemble is trained using active learning, whereby the system itself proposes candidate training instances to be annotated by a human during training. The recognizer was evaluated on both a general collection of newspaper articles and a more focussed, in terms of topics, collection of financial articles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}