Name
Abstract | ||
|---|---|---|
Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most challenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a state. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000. We focus on the temporal role extraction, as a precursor for temporal role disambiguation. We propose a structured prediction approach based on Conditional Random Fields (CRF) to annotate temporal roles in text and rely on a rich feature set, which extracts syntactic and semantic information from text. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.09.021 | Procedia Computer Science |
Keywords | Field | DocType |
Temporal Role Annotation,Sequence Classification | Conditional random field,Annotation,Computer science,Structured prediction,Natural language understanding,Feature set,Natural language,Natural language processing,Artificial intelligence,Ambiguity,Syntax,Machine learning | Conference |
Volume | ISSN | Citations |
137 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koutraki Maria | 1 | 11 | 6.03 |
| Farshad Bakhshandegan Moghaddam | 2 | 23 | 4.35 |
| Harald Sack | 3 | 555 | 72.65 |