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Factuality Assessment as Modal Dependency Parsing ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.122 Abstract: As the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we receive. A critical step towards this goal is to determine the factuality of events in text. In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events. We crowdsource the first large-scale data set annotated with modal dependency structures that consists of 353 Covid-19 related news articles, 24,016 events, and 2,938 conceivers. We also develop the first modal dependency parser that jointly extracts events, conceivers and constructs the modal dependency structure of a text. We evaluate the joint model against a pipeline model and demonstrate the advantage ...
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URL: https://underline.io/lecture/26021-factuality-assessment-as-modal-dependency-parsing
https://dx.doi.org/10.48448/698y-qj72
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Addressing the Data Sparsity Issue in Neural AMR Parsing ...
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GALE English-Chinese Parallel Aligned Treebank -- Training ...
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Two layers of annotation for representing event mentions in news stories
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Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features ...
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A convolution BiLSTM neural network model for Chinese event extraction
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GALE Chinese-English Parallel Aligned Treebank -- Training ...
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