ACE-2005-PT: Corpus for Event Extraction in Portuguese
<|reference_start|>ACE-2005-PT: Corpus for Event Extraction in Portuguese: Event extraction is an NLP task that commonly involves identifying the central word (trigger) for an event and its associated arguments in text. ACE-2005 is widely recognised as the standard corpus in this field. While other corpora, like PropBank, primarily focus on annotating predicate-argument structure, ACE-2005 provides comprehensive information about the overall event structure and semantics. However, its limited language coverage restricts its usability. This paper introduces ACE-2005-PT, a corpus created by translating ACE-2005 into Portuguese, with European and Brazilian variants. To speed up the process of obtaining ACE-2005-PT, we rely on automatic translators. This, however, poses some challenges related to automatically identifying the correct alignments between multi-word annotations in the original text and in the corresponding translated sentence. To achieve this, we developed an alignment pipeline that incorporates several alignment techniques: lemmatization, fuzzy matching, synonym matching, multiple translations and a BERT-based word aligner. To measure the alignment effectiveness, a subset of annotations from the ACE-2005-PT corpus was manually aligned by a linguist expert. This subset was then compared against our pipeline results which achieved exact and relaxed match scores of 70.55\% and 87.55\% respectively. As a result, we successfully generated a Portuguese version of the ACE-2005 corpus, which has been accepted for publication by LDC.<|reference_end|>
@article{cunha2024ace-2005-pt:,
    title={ACE-2005-PT: Corpus for Event Extraction in Portuguese},
    author={Lu'is Filipe Cunha, Purificac{c}~ao Silvano, Ricardo Campos,
  Al'ipio Jorge},
    journal={SIGIR '24: Proceedings of the 47th International ACM SIGIR
  Conference on Research and Development in Information Retrieval (2024)},
    year={2024},
    doi={10.1145/3626772.3657872},
    archivePrefix={arXiv},
    eprint={2408.16928},
    primaryClass={cs.CL cs.AI}
}