Detection of semantic roles associated with linguistic elements is important to the textual classification of communicative context into specific identities. In this paper, a new model for semantically identifying sentences is presented through contextual patterns. The proposed contextual pattern originated its structure from a labeling process of the semantic roles provided by constituents of ...

A large amount of medication information resides in unstructured text in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (e.g., medication, dosage, frequency, mode, etc.). Some medication descriptions contain additional word(s) between medication attributes. Therefore, it is essentia...

This tutorial presents a corpus-driven, pattern-based empirical approach to meaning representation and computation. Patterns in text are everywhere, but techniques for identifying and processing them are still rudimentary. Patterns are not merely syntactic but syntagmatic: each pattern identifies a lexico-semantic clause structure consisting of a predicator (verb or predicative adjective) toget...

This study is part of a project to develop an automatic method to build ontologies, especially in a medical domain, from a document collection. An earlier study had investigated an approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical Language System) semantic net. The study found that semantic relations between concepts could be inferred 68% of the t...

Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs’ roles. These roles are too coarse to represent verbs’ semantics. In this paper, we introduce verb patterns to represent verbs’ semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for ...