Name
Abstract | ||
|---|---|---|
Much of the valuable information in supporting decision making processes originates in text-based documents. Although these documents can be effectively searched and ranked by modern search engines, actionable knowledge need to be extracted and transformed in a structured form before being used in a decision process. In this paper we describe how the discovery of semantic information embedded in natural language documents can be viewed as an optimization problem aimed at assigning a sequence of labels (hidden states) to a set of interdependent variables (textual tokens). Dependencies among variables are efficiently modeled through Conditional Random Fields, an indirected graphical model able to represent the distribution of labels given a set of observations. The Markov property of these models prevent them to take into account long-range dependencies among variables, which are indeed relevant in Natural Language Processing. In order to overcome this limitation we propose an inference method based on Integer Programming formulation of the problem, where long distance dependencies are included through non-deterministic soft constraints. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.ipm.2014.04.005 | Information Processing and Management: an International Journal |
Keywords | Field | DocType |
conditional random fields,integer linear programming,named entity recognition,rule extraction | Data mining,Computer science,Integer programming,Natural language processing,Artificial intelligence,Optimization problem,Conditional random field,Information retrieval,Ranking,Inference,Natural language,Graphical model,Named-entity recognition | Journal |
Volume | Issue | ISSN |
50 | 5 | 0306-4573 |
Citations | PageRank | References |
7 | 0.54 | 35 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabetta Fersini | 1 | 140 | 20.70 |
| Enza Messina | 2 | 214 | 23.18 |
| G. Felici | 3 | 7 | 0.88 |
| Dan Roth | 4 | 7735 | 695.19 |