2,189 research outputs found
Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022
The Event Causality Identification Shared Task of CASE 2022 involved two
subtasks working on the Causal News Corpus. Subtask 1 required participants to
predict if a sentence contains a causal relation or not. This is a supervised
binary classification task. Subtask 2 required participants to identify the
Cause, Effect and Signal spans per causal sentence. This could be seen as a
supervised sequence labeling task. For both subtasks, participants uploaded
their predictions for a held-out test set, and ranking was done based on binary
F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes
the work of the 17 teams that submitted their results to our competition and 12
system description papers that were received. The best F1 scores achieved for
Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing
approaches involved pre-trained language models fine-tuned to the targeted
task. We further discuss these approaches and analyze errors across
participants' systems in this paper.Comment: Accepted to the 5th Workshop on Challenges and Applications of
Automated Extraction of Socio-political Events from Text (CASE 2022
Extracting Temporal and Causal Relations between Events
Structured information resulting from temporal information processing is
crucial for a variety of natural language processing tasks, for instance to
generate timeline summarization of events from news documents, or to answer
temporal/causal-related questions about some events. In this thesis we present
a framework for an integrated temporal and causal relation extraction system.
We first develop a robust extraction component for each type of relations, i.e.
temporal order and causality. We then combine the two extraction components
into an integrated relation extraction system, CATENA---CAusal and Temporal
relation Extraction from NAtural language texts---, by utilizing the
presumption about event precedence in causality, that causing events must
happened BEFORE resulting events. Several resources and techniques to improve
our relation extraction systems are also discussed, including word embeddings
and training data expansion. Finally, we report our adaptation efforts of
temporal information processing for languages other than English, namely
Italian and Indonesian.Comment: PhD Thesi
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