Name
Title | ||
|---|---|---|
Selecting actions for resource-bounded information extraction using reinforcement learning |
Abstract | ||
|---|---|---|
Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to do so under resource limitations. We formulate the information gathering task as a series of choices among alternative, resource-consuming actions and use reinforcement learning to select the best action at each time step. We use temporal difference q-learning method to train the function that selects these actions, and compare it to an online, error-driven algorithm called SampleRank. We present a system that finds information such as email, job title and department affiliation for the faculty at our university, and show that the learning-based approach accomplishes this task efficiently under a limited action budget. Our evaluations show that we can obtain 92.4% of the final F1, by only using 14.3% of all possible actions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2124295.2124328 | WSDM |
Keywords | Field | DocType |
final f1,resource-consuming action,limited action budget,department affiliation,specific information,best action,error-driven algorithm,possible action,use reinforcement,reinforcement learning,information gathering task,resource-bounded information extraction,selecting action,temporal difference,information extraction,web mining | Data mining,Temporal difference learning,Web mining,Information retrieval,Computer science,Information extraction,Specific-information,Artificial intelligence,Machine learning,Reinforcement learning,Bounded function | Conference |
Citations | PageRank | References |
12 | 0.66 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pallika H. Kanani | 1 | 41 | 1.47 |
| Andrew Kachites McCallumzy | 2 | 19203 | 1588.22 |