Forward stagewise additive modeling for entity ranking in documents

WANG Yan-hua

doi:10.3969/j.issn.1000-5641.2018.01.009

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WANG Yan-hua. Forward stagewise additive modeling for entity ranking in documents[J]. Journal of East China Normal University (Natural Sciences), 2018, (1): 91-102, 145. doi: 10.3969/j.issn.1000-5641.2018.01.009

Citation:

WANG Yan-hua. Forward stagewise additive modeling for entity ranking in documents[J]. Journal of East China Normal University (Natural Sciences), 2018, (1): 91-102, 145. doi: 10.3969/j.issn.1000-5641.2018.01.009

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Forward stagewise additive modeling for entity ranking in documents

doi: 10.3969/j.issn.1000-5641.2018.01.009

WANG Yan-hua

School of Data Science and Engineering, East China Normal University, EDWEI Shanghai 200062, China

Received Date: 2016-12-01
Publish Date: 2018-01-25

Abstract

Abstract

Key entities of a document can help to summarize the subjects of the events or the topics that the document describes, which can contribute to applications such as entity-oriented information retrieval and question-answering. However, entities in free text are unordered and hence it is important to rank entities of a document. In this paper, firstly, we make full use of features of entities that extracted from the document and draw support from Wikipedia and Word Embedding to generate external features. Then, we propose a novel ranking model named LA-FSAM(FSAM based on AUC Metric and Logistic Function) which is based on forward stagewise algorithm additive modeling. In LA-FSAM, we employ the AUC(Area Under the Curve) metric to construct the loss function and the logistic function to integrate features of entities. Finally, the stochastic gradient descent is utilized to optimize parameters of LA-FSAM model. After experiments, our evaluation shows the efficiency of the model we proposed.
- entity ranking,
- forward stagewise additive modeling,
- area under the curve,
- logistic function,
- stochastic gradient descent

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References(25)

References

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