Semantic Classification of Bio-Entities Incorporating Predicate-Argument Features
نویسندگان
چکیده
منابع مشابه
A Semantic Kernel for Predicate Argument Classification
Automatically deriving semantic structures from text is a challenging task for machine learning. The flat feature representations, usually used in learning models, can only partially describe structured data. This makes difficult the processing of the semantic information that is embedded into parse-trees. In this paper a new kernel for automatic classification of predicate arguments has been d...
متن کاملNoun Classification from Predicate-Argument Structures
A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, ...
متن کاملSemantic Argument Classification Exploiting Argument Interdependence
This paper describes our research on automatic semantic argument classification, using the PropBank data [Kingsbury et al., 2002]. Previous research employed features that were based either on a full parse or shallow parse of a sentence. These features were mostly based on an individual semantic argument and the relation between the predicate and a semantic argument, but they did not capture th...
متن کاملSemantic Features in Argument Selection
One of the problems that has to be dealt with by theorists of early language acquisition theory is the mismatch between semantic constructs, like Agent, and syntactic ones, like subject. It is proposed that the linguistic system is based on semantic features that are more fine-grained than thematic roles, and that selection of subject and direct object can be accounted for by merely four semant...
متن کاملTransition-based Semantic Role Labeling Using Predicate Argument Clustering
This paper suggests two ways of improving semantic role labeling (SRL). First, we introduce a novel transition-based SRL algorithm that gives a quite different approach to SRL. Our algorithm is inspired by shift-reduce parsing and brings the advantages of the transitionbased approach to SRL. Second, we present a self-learning clustering technique that effectively improves labeling accuracy in t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2008
ISSN: 0916-8532,1745-1361
DOI: 10.1093/ietisy/e91-d.4.1211