Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.
Leveraging linked entities to estimate focus time of short texts / Morbidoni, C.; Cucchiarelli, A.; Ursino, D.. - (2018), pp. 282-285. (Intervento presentato al convegno International Database Engineering & Applications Symposium (IDEAS 2018) tenutosi a Villa San Giovanni, Italy nel Giugno 2018) [10.1145/3216122.3216158].
Leveraging linked entities to estimate focus time of short texts
C. Morbidoni
;A. Cucchiarelli;D. Ursino
2018-01-01
Abstract
Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
