Name
Abstract | ||
|---|---|---|
Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, cross-reference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided. |
Year | Venue | Field |
|---|---|---|
2019 | north american chapter of the association for computational linguistics | Computer science,Artificial intelligence,Natural language processing,Topic model |
DocType | Volume | Citations |
Journal | abs/1905.07508 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Lund | 1 | 13 | 3.81 |
| Piper Armstrong | 2 | 0 | 0.34 |
| Wilson Fearn | 3 | 0 | 1.35 |
| Stephen Cowley | 4 | 0 | 1.01 |
| Emily Hales | 5 | 0 | 0.34 |
| Kevin D. Seppi | 6 | 335 | 41.46 |