Name
Title | ||
|---|---|---|
Learning noun-modifier semantic relations with corpus-based and WordNet-based features |
Abstract | ||
|---|---|---|
We study the performance of two representations of word meaning in learning noun-modifier semantic relations. One representation is based on lexical resources, in particular WordNet, the other - on a corpus. We experimented with decision trees, instance-based learning and Support Vector Machines. All these methods work well in this learning task. We report high precision, recall and F-score, and small variation in performance across several 10-fold cross-validation runs. The corpus-based method has the advantage of working with data without word-sense annotations and performs well over the baseline. The WordNet-based method, requiring word-sense annotated data, has higher precision. |
Year | Venue | Keywords |
|---|---|---|
2006 | AAAI | noun-modifier semantic relation,corpus-based method,instance-based learning,support vector machines,higher precision,10-fold cross-validation,word-sense annotation,decision tree,word-sense annotated data,wordnet-based feature,high precision,wordnet-based method,instance based learning,support vector,cross validation,noun |
Field | DocType | Citations |
Decision tree,Instance-based learning,Information retrieval,Computer science,Noun,Support vector machine,Natural language processing,Artificial intelligence,WordNet,Recall | Conference | 32 |
PageRank | References | Authors |
3.24 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vivi Nastase | 1 | 523 | 41.30 |
| Jelber Sayyad-Shirabad | 2 | 33 | 3.59 |
| Marina Sokolova | 3 | 298 | 21.21 |
| Stan Szpakowicz | 4 | 1200 | 114.50 |