Name
Abstract | ||
|---|---|---|
To mine large digital libraries in humanistically meaningful ways, we need to divide them by genre. This is a task that classification algorithms are well suited to assist, but they need adjustment to address the specific challenges of this domain. Digital libraries pose two problems of scale not usually found in the article datasets used to test these algorithms. 1) Because libraries span several centuries, the genres being identified may change gradually across the time axis. 2) Because volumes are much longer than articles, they tend to be internally heterogeneous, and the classification task also requires segmentation. We describe a multilayered solution that trains hidden Markov models to segment volumes, and uses ensembles of overlapping classifers to address historical change. We demonstrate this on a collection of 469,200 volumes drawn from HathiTrust Digital Library. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BigData.2013.6691676 | Silicon Valley, CA |
Keywords | Field | DocType |
classification,data mining,digital libraries,hidden Markov models,library automation,HathiTrust digital library,article datasets,classification algorithms,classification task,digital libraries mining,hidden Markov models,historical change,mutable genres mapping,overlapping classifers,structurally complex volumes,volumes segmentation | Data mining,Segmentation,Computer science,Natural language processing,Artificial intelligence,Digital library,Library automation,Statistical classification,Hidden Markov model,Machine learning | Journal |
Volume | ISSN | Citations |
abs/1309.3323 | 2639-1589 | 4 |
PageRank | References | Authors |
0.45 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Underwood | 1 | 4 | 0.45 |
| Michael L. Black | 2 | 4 | 0.45 |
| Loretta Auvil | 3 | 147 | 13.64 |
| Boris Capitanu | 4 | 48 | 6.49 |