Name
Abstract | ||
|---|---|---|
This paper describes a system which uses entity and topic coherence for improved Text Segmentation (TS) accuracy. First, Linear Dirichlet Allocation (LDA) algorithm was used to obtain topics for sentences in the document. We then performed entity mapping across a window in order to discover the transition of entities within sentences. We used the information obtained to support our LDA-based boundary detection for proper boundary adjustment. We report the significance of the entity coherence approach as well as the superiority of our algorithm over existing works. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-52941-7_18 | PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS 2016) |
Keywords | Field | DocType |
Text segmentation,Entity coherence,Linear dirichlet allocation,Topic modeling | Pattern recognition,Computer science,Coherence (physics),Text segmentation,Boundary detection,Natural language processing,Artificial intelligence,Dirichlet distribution,Topic model | Conference |
Volume | ISSN | Citations |
552 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kolawole John Adebayo | 1 | 0 | 1.01 |
| Luigi Di Caro | 2 | 195 | 35.21 |
| Guido Boella | 3 | 1867 | 162.59 |