Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferen...Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferential event mentions. To address the above issues, this paper proposes a trigger semantics augmentation mechanism to boost event coreference resolution. First, this mechanism performs a trigger-oriented masking strategy to pre-train a BERT (Bidirectional Encoder Representations from Transformers)-based encoder (Trigger-BERT), which is fine-tuned on a large-scale unlabeled dataset Gigaword. Second, it combines the event semantic relations from the Trigger-BERT encoder with the event interactions from the soft-attention mechanism to resolve event coreference. Experimental results on both the KBP2016 and KBP2017 datasets show that our proposed model outperforms several state-of-the-art baselines.展开更多
We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries.The corpus contains document-,sentence-,and token-level annotations.Th...We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries.The corpus contains document-,sentence-,and token-level annotations.This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information,constructing knowledge bases that enable comparative social and political science studies.For each news source,the annotation starts with random samples of news articles and continues with samples drawn using active learning.Each batch of samples is annotated by two social and political scientists,adjudicated by an annotation supervisor,and improved by identifying annotation errors semi-automatically.We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting,contributing to the generalizability and robustness of automated text processing systems.This corpus and the reported results will establish a common foundation in automated protest event collection studies,which is currently lacking in the literature.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61836007 and 61772354.
文摘Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferential event mentions. To address the above issues, this paper proposes a trigger semantics augmentation mechanism to boost event coreference resolution. First, this mechanism performs a trigger-oriented masking strategy to pre-train a BERT (Bidirectional Encoder Representations from Transformers)-based encoder (Trigger-BERT), which is fine-tuned on a large-scale unlabeled dataset Gigaword. Second, it combines the event semantic relations from the Trigger-BERT encoder with the event interactions from the soft-attention mechanism to resolve event coreference. Experimental results on both the KBP2016 and KBP2017 datasets show that our proposed model outperforms several state-of-the-art baselines.
基金funded by the European Research Council(ERC)Starting Grant 714868 awarded to Dr.Erdem Yörük for his project Emerging Welfare。
文摘We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries.The corpus contains document-,sentence-,and token-level annotations.This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information,constructing knowledge bases that enable comparative social and political science studies.For each news source,the annotation starts with random samples of news articles and continues with samples drawn using active learning.Each batch of samples is annotated by two social and political scientists,adjudicated by an annotation supervisor,and improved by identifying annotation errors semi-automatically.We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting,contributing to the generalizability and robustness of automated text processing systems.This corpus and the reported results will establish a common foundation in automated protest event collection studies,which is currently lacking in the literature.
