Name
Abstract | ||
|---|---|---|
We present a vector space model that supports the computation of appropriate vector representations for words in context, and apply it to a paraphrase ranking task. An evaluation on the SemEval 2007 lexical substitution task data shows promising results: the model significantly outperforms a current state of the art model, and our treatment of context is effective. |
Year | Venue | Keywords |
|---|---|---|
2009 | TextInfer@ACL | appropriate vector representation,paraphrase ranking task,vector space model,ranking paraphrase,lexical substitution task data,art model,current state |
Field | DocType | Volume |
SemEval,Ranking,Computer science,Paraphrase,Artificial intelligence,Natural language processing,Vector space model,Machine learning,Computation | Conference | W09-25 |
Citations | PageRank | References |
16 | 0.89 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Thater | 1 | 756 | 38.54 |
| Georgiana Dinu | 2 | 510 | 33.36 |
| Manfred Pinkal | 3 | 1116 | 69.77 |