A Alejandro Montes Garcia

A DSL based on CSS for hypertext adaptation

Personalization offered by Adaptive Hypermedia and Recommender Systems is effective for tackling the information overload problem. However, the development of Adaptive Web-Based Systems is cumbersome. In order to ease the development of such systems, we propose a language based on CSS to express personalization in web systems that captures current adaptation techniques.

Classification of historical notary acts with noisy labels

This paper approaches the problem of automatic classification of real-world historical notary acts from the 14th to the 20th century. We deal with category ambiguity, noisy labels and imbalanced data. Our goal is to assign an appropriate category for each notary act from the archive collection. We investigate a variety of existing techniques and describe a framework for dealing with noisy labels which includes category resolution, evaluation of inter-annotator agreement and the application of a two level classification. The maximum accuracy we achieve is 88%, which is comparable to the agreement between human annotators.

Who Are My Ancestors? Retrieving Family Relationships from Historical Texts

This paper presents an approach for automatically retrieving family relationships from a real-world collection of Dutch historical notary acts. We aim to retrieve relationships like husband - wife, parent - child, widow of, etc. Our approach includes person names extraction, reference disambiguation, candidate generation and family relationship prediction. Since we have a limited amount of training data, we evaluate different feature configurations based on the n-gram analysis. The best results were obtained by using a combination of bi-grams and tri-grams of words together with the distance in words between two names. We evaluate our results for each type of the relationships in terms of precision, recall and \(f-score\).

Effects of Evolutionary Linguistics in Text Classification

We perform an empirical study to explore the role of evolutionary linguistics on the text classification problem. We conduct experiments on a real-world collection with more than 100.000 Dutch historical notary acts. The document collection spans over six centuries. During such a large time period some lexical terms modified significantly. Person names, professions and other information changed over time as well. Standard text classification techniques which ignore temporal information of the documents might not produce the most optimal results in our case. Therefore, we analyse the temporal aspects of the corpus. We explore the effect of training and testing the model on different time periods. We use time periods that correspond to the main historical events and also apply clustering techniques in order to create time periods in a data driven way. All experiments show a strong time-dependency of our corpus. Exploiting this dependence, we extend standard classification techniques by combining different models trained on particular time periods and achieve overall accuracy above

The impact of technical domain expertise on search behavior and task outcome

90\,\%

WiBAF into a CMS: personalization in learning environments made easy

and macro-average indicators abovei?ź63i?ź%.