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Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations

AAAI 2016 · Label-efficient and robust learning › Recognition and long-tail · approach: Recommendation representation learning · 141 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add similarity + association recommendation (AER) over DPDP · Hu&Liu D1-D5 avg (multiple occurrences)F1: 79.1 -> 87.0Helped
add similarity + association recommendation (AER) over DPDP · Liu2015 D6-D8 avg (distinct terms)F1: 67.9 -> 73.3Helped
add association recommendation to similarity-only (SimR -> AER) (ablation)AER · Hu&Liu D1-D5 avg (distinct terms)F1: 72.8 -> 75.9Helped
add similarity recommendation to association-only (ARR -> AER) (ablation)AER · Hu&Liu D1-D5 avg (distinct terms)F1: 60.1 -> 75.9Helped
word-vector similarity recommendation on DP- base (SimR) (ablation)DP- · Hu&Liu D1-D5 avg (multiple occurrences)F1: 72.6 -> 85.0Helped
association recommendation on DP- base (ARR) (ablation)DP- · Hu&Liu D1-D5 avg (multiple occurrences)F1: 72.6 -> 80.4Helped
add more dependency relations to rules (DP -> DP+)DP · Hu&Liu D1-D5 avg (multiple occurrences)F1: 79.1 -> 76.4Hurt
keep only high-precision rules (DP -> DP-)DP · Hu&Liu D1-D5 avg (multiple occurrences)F1: 79.1 -> 72.6Hurt
word vectors instead of WordNet similarity (SYN, WUP, LIN, PATH) (ablation)SimR · D1-D8 avgF1Helped
mine association rules from 100 domains instead of 50 electronic domains (ablation)AER · D1-D8 avgF1Helped

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