Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction - NLP

Explore a 14-minute conference talk from SIGIR 2024 focused on Natural Language Processing (NLP) and aspect sentiment triplet extraction. Delve into the research presented by authors You Li, Xupeng Zeng, Yixiao Zeng, and Yuming Lin on an enhanced packed marker approach that incorporates entity information. Learn about the latest advancements in sentiment analysis techniques and how this novel method improves the extraction of aspect-sentiment-opinion triplets from text data. Gain insights into the potential applications and implications of this research for various NLP tasks and sentiment analysis in particular.

SIGIR 2024 M2.5 [fp] Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Ext