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Found 17 papers, 2 papers with code
KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification
Text classification has wide-ranging applications in various domains.
COMET-M: Reasoning about Multiple Events in Complex Sentences
We propose COMET-M (Multi-Event), an event-centric commonsense model capable of generating commonsense inferences for a target event within a complex sentence.
What happens before and after: Multi-Event Commonsense in Event Coreference Resolution
Event coreference models cluster event mentions pertaining to the same real-world event.
A High Precision Pipeline for Financial Knowledge Graph Construction
This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342, 000 compact extractions from over 288, 000 financial news articles, with a precision of 78{\%} at the top-100 extractions. The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.
Computational Skills by Stealth in Secondary School Data Science
The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang proposed integration of computing concepts into statistics curricula at all levels in statistics and new data science programs and courses.
Dense Forecasting of Wildfire Smoke Particulate Matter Using Sparsity Invariant Convolutional Neural Networks
Accurate forecasts of fine particulate matter (PM 2. 5) from wildfire smoke are crucial to safeguarding cardiopulmonary public health.
Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media
Success of the rigorous annotation process on identifying stigma is reconfirmed by achieving high prediction rate with CNN.
Discourse Analysis and Its Applications
Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many downstream applications.
Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis
Discourse Parsing and Sentiment Analysis are two fundamental tasks in Natural Language Processing that have been shown to be mutually beneficial.
Ranked #26 on
Sentiment Analysis
on SST-5 Fine-grained classification
Generating and Evaluating Summaries for Partial Email Threads: Conversational Bayesian Surprise and Silver Standards
We define and motivate the problem of summarizing partial email threads.
Training Data Enrichment for Infrequent Discourse Relations
We propose a training data enrichment framework that relies on co-training of two different discourse parsers on unlabeled documents.
