1. WordNetPlus C. Fellbaum, D. Osherson, R. Schapire, M. Charikar, C. Basu and 24 Princeton Undergraduates Funded by NSF/IIS

2. Motivation Create a more useful WordNet by adding --arcs among all nodes --weighted arcs --directed arcs --arcs are based on human ratings of strength of evocation Goal: completely connected network (WNPlus) to beat current sparsity problem

3. Evocation Annotations Select 1,000 “core” (salient, frequent) synsets from WN Randomly choose 120,000 pairs from core set UGs rate, on a scale from 0-100 “how strongly does concept1 evoke concept2?” Interface allows annotation of all integers

4. Annotations --Note that annotations are directional, so evocation between concept1 and concept2 may differ from that between concept2 and concept1 --Evocation differs from, and overlaps only partially with, Exemplification (chair-furniture) Application (wolf-ferocious)

5. Annotations Annotators were discouraged from considering non-semantic criteria (e.g. rhyme) Annotators reproduced “typical American adult” Training and testing on 500 synset pairs (each) previously rated by the investigators

6. Results Each synset pair was rated by at least 3 annotators Each annotator was given >100 pairs twice average intra-annotator correlation: 0.70 Pearson correlation between annotators’ and investigators’ ratings: median.72; none lower than.64

7. Future Work Weighted, directed arcs among the remaining synsets will be learned Features: manual ratings, textual co- occurrence, WN similarity, LSA Direct and indirect evaluation of WNPlus