Name
Abstract | ||
|---|---|---|
Questioning has been shown to improve learning outcomes, and automatic question generation can greatly facilitate the inclusion of questions in learning technologies such as intelligent tutoring systems. The majority of prior QG systems use parsing software and transformation algorithms to create questions. In contrast, the approach described here infuses natural language understanding NLU into the natural language generation NLG process by first analyzing the central semantic content of each independent clause in each sentence. Then question templates are matched to what the sentence is communicating in order to generate higher quality questions. This approach generated a higher percentage of acceptable questions than prior state-of-the-art systems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-39583-8_3 | ITS |
Field | DocType | Volume |
Natural language generation,Computer science,Natural language understanding,Independent clause,Software,Natural language processing,Artificial intelligence,Automatic question generation,Parsing,Sentence | Conference | 9684 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karen Mazidi | 1 | 17 | 1.94 |
| Paul Tarau | 2 | 1529 | 113.14 |