Name
Abstract | ||
|---|---|---|
Prepositional phrases (PPs) express crucial information that knowledge base construction methods need to extract. However, PPs are a major source of syntactic ambiguity and still pose problems in parsing. We present a method for resolving ambiguities arising from PPs, making extensive use of semantic knowledge from various resources. As training data, we use both labeled and unlabeled data, utilizing an expectation maximization algorithm for parameter estimation. Experiments show that our method yields improvements over existing methods including a state of the art dependency parser. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1 | Semantic memory,Expectation–maximization algorithm,Computer science,Phrase,Dependency grammar,Artificial intelligence,Natural language processing,Knowledge base,Estimation theory,Syntactic ambiguity,Parsing |
DocType | Volume | Citations |
Conference | P15-1 | 4 |
PageRank | References | Authors |
0.42 | 27 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ndapandula Nakashole | 1 | 394 | 19.48 |
| Tom M. Mitchell | 2 | 7160 | 1946.42 |