Name
Abstract | ||
|---|---|---|
Although employees' expertise has for some time been seen as a vital knowledge asset in organisations, it is only lately that it started to attract researchers' attention. As a result, interest in automated systems that aim at enhancing the visibility and traceability of employees with particular expertise is growing. This research focuses on one critical everyday organisational business tool - email, as an information source to help locate employees with particular expertise within the organisation. This paper presents the process for keyphrase extraction from email messages. The process uses machine learning to tag new text by its part of speech, then extracts keyphrases purely based on part-of-speech (POS) tags that surround these phrases. The system has been evaluated using three datasets. Results show that the use of the linguistic tool, WordNet, improves to some extent the precision, recall, and f-measure metrics. The goal of this work is to advance our understanding of what may (or may not) be effective in extracting information from email to help identify experts. |
Year | Venue | Keywords |
|---|---|---|
2007 | ECIS | performance measurement,knowledge management |
Field | DocType | Citations |
Computer science,Knowledge management,Performance measurement,Knowledge extraction | Conference | 1 |
PageRank | References | Authors |
0.35 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Tedmori | 1 | 56 | 7.13 |
| Thomas W. Jackson | 2 | 217 | 17.90 |
| Dino Bouchlagem | 3 | 1 | 0.35 |