Automatic Acquisition of Semantics-Extraction Patterns
10220Authors
nan
Abstract
AbstractThis paper examines the use of parallel and comparable corpora for automatic acquisition of semantics-extraction patterns. It presents a new method of the pattern extraction which takes advantage of parallel texts to "port" text mining solutions from a source language to a target language. It is shown thatthe technique can help in situations when the extraction procedure is to beapplied in a language (languages) with a limited set of available resources,e.g. domain-specific thesauri. The primary motivation of our work lies in a particular multilingual e-learning system. For testing purposes, other applications of the given approach were implemented. They include pattern extraction from general texts (tested on wordnet relations), acquisition of domain-specific patterns from large parallel corpus of legal EU documents, and mining of subjectivity expressions for multilingual opinion extraction system.
Keywords
Full Content
nan
Labeled Text
AbstractThis paper examines the use of parallel and comparable corpora for automatic acquisition of semantics-extraction patterns. It presents a new method of the pattern extraction which takes advantage of parallel texts to "port" text mining solutions from a source language to a target language. It is shown thatthe technique can help in situations when the extraction procedure is to beapplied in a language (languages) with a limited set of available resources,e.g. domain-specific thesauri. The primary motivation of our work lies in a particular multilingual e-learning system. For testing purposes, other applications of the given approach were implemented. They include pattern extraction from general texts (tested on wordnet relations), acquisition of domain-specific patterns from large parallel corpus of legal EU documents, and mining of subjectivity expressions for multilingual opinion extraction system.
Publication Details
- Paper ID:
- 10220
- Year:
- 2006
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan