Independent semantic feature extraction algorithm based on short text

HU Jia-ni; GUO Jun; DENG Wei-hong; XU Wei-ran

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Independent semantic feature extraction algorithm based on short text

更新时间：2024-10-14

- Independent semantic feature extraction algorithm based on short text
- Issue 12, Pages: 121-124(2007)
- 作者机构：
  
  1. 北京邮电大学信息工程学院
  2. 北京邮电大学信息工程学院,北京,100876
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.1
- Published：2007
- 稿件说明：
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HU Jia-ni, GUO Jun, DENG Wei-hong, et al. Independent semantic feature extraction algorithm based on short text[J]. 2007, (12): 121-124.
DOI：

HU Jia-ni, GUO Jun, DENG Wei-hong, et al. Independent semantic feature extraction algorithm based on short text[J]. 2007, (12): 121-124. DOI：

摘要

提出了一种基于短文本的独立语义特征抽取算法

旨在降低文本向量的稀疏性并提其高语义表示能力。该算法首先采用潜在语义分析降低文本的维数并去除噪声

然后运用独立成份分析方法在潜在语义特征中提取出最能表达语义且相互统计独立的特征。实验表明此算法优于潜在语义索引算法。

Abstract

An independent semantic feature extraction algorithm was proposed

aiming at reducing the sparseness of short text and enhancing its capability of semantic expression.The algorithm first makes use of latent semantic indexing to re-duce the dimension and wipe off noise

and then it introduces independent component analysis to extract statistic inde-pendent and semantic features.Experimental results prove the feasibility of the algorithm and demonstrate it is superior to latent semantic indexing.

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