Name
Abstract | ||
|---|---|---|
In this paper, we address the problem of quantifying the overall extent to which a testtaker’s essay deals with the topic it is assigned (prompt). We experiment with a number of models for word topicality, and a number of approaches for aggregating word-level indices into text-level ones. All models are evaluated for their ability to predict the holistic quality of essays. We show that the best texttopicality model provides a significant improvement in a state-of-art essay scoring system. We also show that the findings of the relative merits of different models generalize well across three different datasets. |
Year | Venue | Field |
|---|---|---|
2016 | BEA@NAACL-HLT | Computer science,Artificial intelligence,Natural language processing,Machine learning,Scoring system |
DocType | Citations | PageRank |
Conference | 2 | 0.36 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beata Beigman Klebanov | 1 | 137 | 19.49 |
| Michael Flor | 2 | 34 | 8.18 |
| Binod Gyawali | 3 | 27 | 5.44 |