Tree-Structured Conditional Random Fields for Semantic Annotation

Tang, Jie; Hong, Min-Cheol; Li, Juanzi; Liang, Bin

doi:10.1007/11926078_46

Cited by 33 publications

(22 citation statements)

References 15 publications

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“…The unified approach for research profiling explored in this paper is based on a Tree-structured Conditional Random Fields (TCRFs) (Tang et al 2006).…”

Section: Data Preparationmentioning

confidence: 99%

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Topic level expertise search over heterogeneous networks

et al. 2010

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In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of Learn (2011) 82: 211-237 work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.

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“…The unified approach for research profiling explored in this paper is based on a Tree-structured Conditional Random Fields (TCRFs) (Tang et al 2006).…”

Section: Data Preparationmentioning

confidence: 99%

“…As the tagging model, we use Tree-structured Conditional Random Fields (TCRFs) (Tang et al 2006). TCRFs can model dependencies across hierarchically laid-out information.…”

Section: Fig 1 the Schema Of Academic Networkmentioning

confidence: 99%

Topic level expertise search over heterogeneous networks

et al. 2010

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“…The density of the point clouds also varies with respect to the different distance from the laser scanner. In order to solve the insufficient constraints in the local smoothing interaction in the image labeling problem, tree structured [11,12] and multi-scaled [13] approaches are introduced to the CRFs. Our work is based on the multi-scale CRF approach and we extended the approach for 3D data labeling.…”

Section: 11mentioning

confidence: 99%

Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification

Lim

Suter

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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“…Schutz and Buitelaar [27] described RelExt for extracting relations in the football domain. Tang et al [10] proposed Tree-CRF for semantic annotation on semi-structured data. Ramakrishnan et al [2] described a schemadriven approach to relation extraction from biomedical text.…”

Section: Related Workmentioning

confidence: 99%

PORE: Positive-Only Relation Extraction from Wikipedia Text

Wang

Zhu

2007

The Semantic Web

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Abstract. Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.

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Tree-Structured Conditional Random Fields for Semantic Annotation

Cited by 33 publications

References 15 publications

Topic level expertise search over heterogeneous networks

Topic level expertise search over heterogeneous networks

Multi-scale Conditional Random Fields for over-segmented irregular 3D point clouds classification

PORE: Positive-Only Relation Extraction from Wikipedia Text

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