Summary of the paper

Title HiEve: A Corpus for Extracting Event Hierarchies from News Stories
Authors Goran Glavaš, Jan Šnajder, Marie-Francine Moens and Parisa Kordjamshidi
Abstract In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent―subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter annotator agreement.
Topics Information Extraction, Information Retrieval, Text Mining
Full paper HiEve: A Corpus for Extracting Event Hierarchies from News Stories
Bibtex @InProceedings{GLAVA14.1023,
  author = {Goran Glavaš and Jan Šnajder and Marie-Francine Moens and Parisa Kordjamshidi},
  title = {HiEve: A Corpus for Extracting Event Hierarchies from News Stories},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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