Name
Abstract | ||
|---|---|---|
In news stories verbatim quotes of persons play a very important role, as they carry reliable information about the opinion of that person concerning specific aspects. As thousands of new quotes are published every hour it is very difficult to keep track of them. In this paper we describe a set of algorithms to solve the knowledge management problem of identifying, storing and accessing verbatim quotes. We handle the verbatim quote task as a relation extraction problem from unstructured text. Using a workflow of knowledge extraction algorithms we provide the required features for the relation extraction algorithm. The central relation extraction procedures is trained using manually annotated documents. It turns out that structural grammatical information is able to improve the F-vale for verbatim quote detection to 84.1%, which is sufficient for many exploratory applications. We present the results in a smartphone app connected to a web server, which employs a number of algorithms like linkage to Wikipedia, topics extraction and search engine indices to provide a flexible access to the extracted verbatim quotes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2362456.2362495 | I-KNOW |
Keywords | Field | DocType |
verbatim quote task,verbatim quote,present verbatim quote,knowledge management problem,verbatim quote detection,central relation extraction procedure,free text,knowledge-extraction approach,knowledge extraction,news stories verbatim quote,topics extraction,relation extraction problem,relation extraction algorithm,text mining,information extraction,natural language processing,relation extraction,search engine,knowledge management | Information retrieval,Computer science,Natural language processing,Knowledge extraction,Artificial intelligence,Workflow,Relationship extraction,Web server | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerhard Paass | 1 | 1136 | 83.63 |
| Andre Bergholz | 2 | 0 | 0.68 |
| Anja Pilz | 3 | 32 | 2.83 |