Forward stagewise additive modeling for entity ranking in documents

doi:10.3969/j.issn.1000-5641.2018.01.009

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.

Key words: entity ranking, forward stagewise additive modeling, area under the curve, logistic function, stochastic gradient descent

CLC Number:

P311

WANG Yan-hua. Forward stagewise additive modeling for entity ranking in documents[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(1): 91-102,145.

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