Multi - granularity Relation Extraction of Text Semi - supervised Adaptive Learning View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2012-2015

FUNDING AMOUNT

240000 CNY

ABSTRACT

Automatic extraction of semantic relations in text is an important research content of the text mining and machine learning.This project aims to establish a new semi-supervised adaptive learning framework for multi-granularity relationship extraction, and applies to protein-protein interaction relation extraction in biomedical literature. The project's main research topics include: (1) To propose a theoretical framework of improved heuristic fast semi-supervised support vector machines,which adds new content for the efficient and large-scale semi-supervised learning; (2)To build a new multi-granularity adaptive classification model, which integrates active learning and semi-supervised learning and proposes a new adaptive learning theory framework; (3) To establish a multi-granularity multi-classifier to do relation extraction task. Moreover, this classifier can be applied to other application areas with a large number of unlabeled samples and high dimensional feature vectors; (4) To apply the proposed theoretical model in the protein-protein interaction relation extraction of text mining study. Make use of the integration of semi-supervised learning and active learning, extracting the rich, multi-granularity features based on natural language structure and biological domain information, a new machine learning f More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=61202135

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