Name
Abstract | ||
|---|---|---|
Semantic Role Labeling (SRL) plays a key role in many NLP applications. The development of SRL systems for the biomedical domain is frustrated by the lack of large domain-specific corpora that are labeled with semantic roles. Corpus development has been very expensive and time-consuming. In this paper we propose a method for building frame-based corpus on the basis of domain knowledge provided by ontologies. We believe that ontologies, as a structured and semantic representation of domain knowledge, can instruct and ease the tasks in building the corpora. In the paper we present a corpus built by using the method. We compared it to BioFrameNet, and examined the gaps between the semantic classification of the target words in the domain-specific corpus and in FrameNet and Prop-Bank/VerbNet. |
Year | Venue | Keywords |
|---|---|---|
2011 | BioNLP@ACL | biomedical domain,large domain-specific corpus,frame-based corpus,domain-specific corpus,semantic representation,semantic role,ontological domain knowledge,srl system,domain knowledge,corpus development,semantic classification,computer science |
Field | DocType | Citations |
Ontology (information science),Ontology,Information retrieval,Domain knowledge,Computer science,Explicit semantic analysis,VerbNet,Frame based,Natural language processing,Artificial intelligence,Semantic role labeling,FrameNet | Conference | 2 |
PageRank | References | Authors |
0.38 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Tan | 1 | 3 | 0.73 |
| Rajaram Kaliyaperumal | 2 | 26 | 6.95 |
| Nirupama Benis | 3 | 3 | 0.73 |