Text re-use describes the spoken and written repetition of information. Historical text re-use, with its longer time span, embraces a larger set of morphological, linguistic, syntactic, semantic and copying variations, thus adding complication to text-reuse detection. Furthermore, it increases the chances of redundancy in a digital library. In Natural Language Processing it is crucial to remove these redundancies before we can apply any kind of machine learning techniques to the text. In Humanities, these redundancies foreground textual criticism and allow scholars to identify lines of transmission. Identification can be accomplished by way of automatic or semi-automatic methods. Text re-use algorithms, however, are of squared complexity and call for higher computational power. The present paper addresses this issue of complexity, with a particular focus on its algorithmic implications and solutions.
Marco, B., Franzini, G., Emily, F., Maria, M., Scaling historical text re-use, Paper, in Proceedings of the 2014 IEEE International Conference on Big Data (Big Data), (Washington, DC, 27-30 October 2014), N/A, Washington, DC 2014: 23-31. 10.1109/BigData.2014.7004449 [http://hdl.handle.net/10807/127330]
Scaling historical text re-use
Franzini, GretaSecondo
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2014
Abstract
Text re-use describes the spoken and written repetition of information. Historical text re-use, with its longer time span, embraces a larger set of morphological, linguistic, syntactic, semantic and copying variations, thus adding complication to text-reuse detection. Furthermore, it increases the chances of redundancy in a digital library. In Natural Language Processing it is crucial to remove these redundancies before we can apply any kind of machine learning techniques to the text. In Humanities, these redundancies foreground textual criticism and allow scholars to identify lines of transmission. Identification can be accomplished by way of automatic or semi-automatic methods. Text re-use algorithms, however, are of squared complexity and call for higher computational power. The present paper addresses this issue of complexity, with a particular focus on its algorithmic implications and solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.