Tag Allocation Model: Model Noisy Social Annotations by Reason Finding

Tag Allocation Model: Model Noisy Social Annotations by Reason Finding
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DOI：
10.1109/wi-iat.2010.85
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发表时间：
2010-08
期刊：
2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
影响因子：
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通讯作者：
Xiance Si;Maosong Sun
Xiance Si;Maosong Sun
中科院分区：
其他
文献类型：
--
作者：
Xiance Si;Maosong Sun

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我们提出了标签分配模型（TAM）来对社会标注数据建模。TAM是一种概率生成模型，其关键特征是找出每个标签的潜在原因。潜在的原因可能是文档的任何离散特征（如单词）或全局噪声变量。推断每个标记的原因有助于TAM减少具有多个标记的文档的模糊性。通过引入噪声作为原因，TAM可以自然地处理噪声标签。我们在三个真实世界的数据集上进行实验。结果表明，TAM在伸出困惑度和标签推荐精度方面都优于最先进的方法。
We propose the Tag Allocation Model (TAM) to model social annotation data. TAM is a probabilistic generative model, its key feature is finding the latent reason for each tag. A latent reason can be any discrete features of the document (such as words) or a global noise variable. Inferring the reason for each tag helps TAM reduce the ambiguity of a document with multiple tags. By introducing noise as a reason, TAM can handle noise tags naturally. We perform experiments on three real world data sets. The results show that TAM outperforms state-of-the-art approaches in both held-out perplexity and tag recommendation accuracy.