Name
Abstract | ||
|---|---|---|
We propose a method for semantic structure analysis of noun phrases using Abstract Meaning Representation (AMR). AMR is a graph representation for the meaning of a sentence, in which noun phrases (NPs) are manually annotated with internal structure and semantic relations. We extract NPs from the AMR corpus and construct a data set of NP semantic structures. We also propose a transition-based algorithm which jointly identifies both the nodes in a semantic structure tree and semantic relations between them. Compared to the baseline, our method improves the performance of NP semantic structure analysis by 2.7 points, while further incorporating external dictionary boosts the performance by 7.1 points. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2 | Noun phrase,Specifier,Computer science,Noun,Artificial intelligence,Natural language processing,Semantic structure analysis,Proper noun,Sentence,Graph (abstract data type) |
DocType | Volume | Citations |
Conference | P15-2 | 3 |
PageRank | References | Authors |
0.42 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuichiro Sawai | 1 | 3 | 0.42 |
| Hiroyuki Shindo | 2 | 75 | 13.80 |
| Yuji Matsumoto | 3 | 27 | 12.98 |