This paper presents a benchmark dataset for the task of inter-sentence relation extraction. The paper explains the distant supervision method followed for creating the dataset for inter-sentence relation extraction, involving relations previously used for standard intra-sentence relation extraction task. The study evaluates baseline models such as bag-of-words and sequence based recurrent neural network models on the developed dataset and shows that recurrent neural network models as more useful for the task of intra-sentence relation extraction.Comparing the results of the present work on intra-sentence relation extraction with previous work on inter-sentence relation extraction, the study identifies the need for more sophisticated models to handle long-range information between entities across sentences.
@InProceedings{MANDYA18.790, author = {Angrosh Mandya and Danushka Bollegala and Frans Coenen and Katie Atkinson}, title = "{A Dataset for Inter-Sentence Relation Extraction using Distant Supervision}", 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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