The digital world generates an incredible accumulation of information. This results in redundant, complementary, and contradictory information, which may be produced by several sources. Applications as multidocument summarization and question answering are committed to handling this information and require the identification of relations among the various texts in order to accomplish their tasks. In this paper we first describe an effort to create and annotate a corpus of news texts with multidocument relations from the Crossdocument Structure Theory (CST) and then present a machine learning experiment for the automatic identification of some of these relations. We show that our results for both tasks are satisfactory.
Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. We show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. We also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.
Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. We show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. We also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.