Name
Title | ||
|---|---|---|
HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees. |
Abstract | ||
|---|---|---|
We present two systems created for SemEval2016s Task 11: Complex Word Identification. Our two systems, a regression tree and decision tree, were trained with a word’s unigram and lemma word counts, average ageof-acquisition, and a measure of concreteness. The systems ranked 5th and 6th, respectively, on the test set by G-score (the harmonic mean between accuracy and recall). With the regression tree’s predictions earning a G-score of 0.766, and the decision tree’s earning 0.765, the two systems scored within 1 percent of the score of the best-performing system in the task. |
Year | Venue | Field |
|---|---|---|
2016 | SemEval@NAACL-HLT | Decision tree,Concreteness,SemEval,Ranking,Computer science,Harmonic mean,Natural language processing,Artificial intelligence,Recall,Machine learning,Lemma (mathematics),Test set |
DocType | Citations | PageRank |
Conference | 3 | 0.43 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maury Quijada | 1 | 3 | 0.43 |
| Julie Medero | 2 | 18 | 4.54 |