In this article we review Temporal Processing systems that participated in the TempEval-3 task as a basis to develop our own system, that we also present and release. The system incorporates high level lexical semantic features, obtaining the best scores for event detection (F1-Class 72.24) and second best result for temporal relation classification from raw text (F1 29.69) when evaluated on the TempEval-3 data. Additionally, we analyse the errors of all TempEval-3 systems for which the output is publicly available with the purpose of finding out what are the weaknesses of current approaches. Although incorporating lexical semantics features increases the performance of our system, the error analysis shows that systems should incorporate inference mechanisms and world knowledge, as well as having strategies to compensate for data skewness.
@InProceedings{CASELLI18.880, author = {Tommaso Caselli and Roser Morante}, title = "{Systems’ Agreements and Disagreements in Temporal Processing: An Extensive Error Analysis of the TempEval-3 Task}", 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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