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Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ...
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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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A Prototype Free/Open-Source Morphological Analyser and Generator for Sakha ...
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Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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Cross-document Event Identity via Dense Annotation ...
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Abstract:
In this paper, we study the identity of textual events from different documents. While the complex nature of event identity is previously studied (Hovy et al., 2013), the case of events across documents is unclear. Prior work on cross-document event coreference has two main drawbacks. First, they restrict the annotations to a limited set of event types. Second, they insufficiently tackle the concept of event identity. Such annotation setup reduces the pool of event mentions and prevents one from considering the possibility of quasi-identity relations. We propose a dense annotation approach for cross-document event coreference, comprising a rich source of event mentions and a dense annotation effort between related document pairs. To this end, we design a new annotation workflow with careful quality control and an easy-to-use annotation interface. In addition to the links, we further collect overlapping event contexts, including time, location, and participants, to shed some light on the relation between ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://underline.io/lecture/39885-cross-document-event-identity-via-dense-annotation https://dx.doi.org/10.48448/ypzy-nn49
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Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys ...
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Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach ...
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Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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