TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

LREC 2018 Tirthankar GhosalAmitra SalamSwati TiwariAsif EkbalPushpak Bhattacharyya

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc. Important though the problem is, we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework... (read more)

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