Search Results for author: Ellen Riloff

Found 28 papers, 4 papers with code

Affective Event Classification with Discourse-enhanced Self-training

1 code implementation EMNLP 2020 Yuan Zhuang, Tianyu Jiang, Ellen Riloff

First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base.

Classification General Classification

Classifying Organizations for Food System Ontologies using Natural Language Processing

no code implementations19 Sep 2023 Tianyu Jiang, Sonia Vinogradova, Nathan Stringham, E. Louise Earl, Allan D. Hollander, Patrick R. Huber, Ellen Riloff, R. Sandra Schillo, Giorgio A. Ubbiali, Matthew Lange

Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population and integration with food system ontologies.

Classification Knowledge Graphs

Learning Prototypical Functions for Physical Artifacts

1 code implementation ACL 2021 Tianyu Jiang, Ellen Riloff

We use frames from FrameNet to represent a set of common functions for objects, and describe a manually annotated data set of physical objects labeled with their prototypical function.

Exploring the Role of Context to Distinguish Rhetorical and Information-Seeking Questions

no code implementations ACL 2020 Yuan Zhuang, Ellen Riloff

Social media posts often contain questions, but many of the questions are rhetorical and do not seek information.

Identifying Affective Events and the Reasons for their Polarity

no code implementations WS 2018 Ellen Riloff

Many events have a positive or negative impact on our lives (e. g., {``}I bought a house{''} is typically good news, but {''}My house burned down{''} is bad news).

Sarcasm Detection Weakly-supervised Learning

Learning Prototypical Goal Activities for Locations

no code implementations ACL 2018 Tianyu Jiang, Ellen Riloff

People go to different places to engage in activities that reflect their goals.

Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog

no code implementations WS 2017 Shereen Oraby, Vrindavan Harrison, Amita Misra, Ellen Riloff, Marilyn Walker

We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0. 76 F1 for "sarcastic" and 0. 77 F1 for "other" in forums, and 0. 83 F1 for both "sarcastic" and "other" in tweets.

Cannot find the paper you are looking for? You can Submit a new open access paper.