Name
Abstract | ||
|---|---|---|
We explore the use of semantic word embeddings in text segmentation algorithms, including the C99 segmentation algorithm and new algorithms inspired by the distributed word vector representation. By developing a general framework for discussing a class of segmentation objectives, we study the effectiveness of greedy versus exact optimization approaches and suggest a new iterative refinement technique for improving the performance of greedy strategies. We compare our results to known benchmarks, using known metrics. We demonstrate state-of-the-art performance for an untrained method with our Content Vector Segmentation (CVS) on the Choi test set. Finally, we apply the segmentation procedure to an in-the-wild dataset consisting of text extracted from scholarly articles in the arXiv.org database. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Iterative refinement,Scale-space segmentation,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Text segmentation,Natural language processing,Artificial intelligence,Machine learning,Test set |
DocType | Volume | Citations |
Journal | abs/1503.05543 | 4 |
PageRank | References | Authors |
0.42 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander A. Alemi | 1 | 70 | 9.92 |
| Paul Ginsparg | 2 | 4 | 0.42 |