Papers with CDC

5 papers
Contextual Domain Classification with Temporal Representations (2021.naacl-industry)

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Challenge: Existing studies that incorporate context in SLU have focused on domains where context is limited to a few minutes.
Approach: They propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup.
Outcome: The proposed model reduces 13.04% of classification errors compared to baseline . previous studies have focused on domains where context is limited to a few minutes .
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval (2021.emnlp-main)

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Challenge: 16K FAQ items scraped from 55 credible websites . 32 human-annotated FAQ items for each query.
Approach: They present a large, challenging dataset for FAQ retrieval for COVID-19 . they use a FAQ bank, Query Bank and Relevance Set to evaluate the dataset .
Outcome: The proposed model achieves 48.8 under P@5 and is compared with other datasets.
Adapting Coreference Resolution for Processing Violent Death Narratives (2021.naacl-main)

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Challenge: Existing coreference models suffer from poortransferability due to domain gaps . existing models are not robust enough to handle text data about LGBT individuals .
Approach: They propose to use a dataaugmentation rule to improve coreference resolution in an administrative database written in English to better handle LGBT data.
Outcome: The proposed model improves perfor-mance and accuracy of coreference resolution in a violent death nar-rative from the Centers for Disease Control's (CDC) national Violent Death Re-porting System.
Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference (2021.acl-long)

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Challenge: Existing approaches to disambiguate mentions of named entities are limited . existing approaches omit details needed to ensure fair comparisons .
Approach: They propose to use streaming CDC to disambiguate mentions of named entities . they compare a set of existing and new datasets to evaluate their models .
Outcome: The proposed system is well-suited for processing streams of data where new entities are frequently introduced.
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data (2024.lrec-main)

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Challenge: Prior research on Twitter has provided positive evidence of its utility in developing supplementary health surveillance systems.
Approach: They propose a framework to surveil public health, focusing on mental health outcomes by using tweets from 765 neighborhoods in the USA.
Outcome: The proposed framework achieves the highest F1-score and accuracy over the previous framework, and extrapolates CDC’s estimates to proxy unreported neighborhoods.

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