Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem. This has immense importance in widespread Natural Language Processing (NLP) applications ranging from extractive text document summarization to tracking development of news events to predicting impact of scholarly articles. Although a very relevant problem in the present context of exponential data duplication, we are unaware of any document level dataset that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework. To bridge this relative gap, here in this work, we present a resource for benchmarking the techniques for document level novelty detection. We create the resource via topic-specific crawling of news documents across several domains in a periodic manner. We release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern.
@InProceedings{GHOSAL18.479, author = {Tirthankar Ghosal and Amitra Salam and Swati Tiwary and Asif Ekbal and Pushpak Bhattacharyya}, title = "{TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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