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Semantic Oppositeness Detection

Semantic oppositeness—the relationship between words, phrases, or concepts that express contrasting meanings—plays a crucial role in understanding nuance, detecting errors, and reasoning over natural language. This project investigates computational methods for automatically detecting and modeling oppositeness relations in text.
We explore embedding-based techniques, including autoencoder-driven models, to encode oppositeness relations in vector spaces. In addition, deep contextual models are integrated with semantic oppositeness signals to enhance their ability to capture contradictions and disagreements in text. These approaches are evaluated for their utility in improving reasoning systems and enhancing semantic error detection.
The research also emphasizes building systematic evaluation strategies and benchmarks to measure the reliability of oppositeness detection across varied linguistic settings. By combining theoretical modeling with applied evaluation, this work contributes toward more robust NLP systems capable of nuanced semantic understanding.

Objectives:

• Develop computational models to identify and represent semantic oppositeness in natural language.
• Investigate embedding-based methods, including autoencoder and neural architectures, for capturing oppositeness relations.
• Apply semantic oppositeness detection to enhance error identification, inconsistency detection, and disagreement recognition in text.
• Integrate contextual modeling techniques with oppositeness detection to improve robustness in downstream NLP tasks.
• Create benchmarks and evaluation methods to systematically assess the effectiveness of semantic oppositeness approaches.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Ontologies | Big Data | Bioinformatics |

Publications

Team

External Collaborators: | Fernando Gutierrez | Stephen Fickas |