Word Sense Disambiguation for All Words using Tree-Structured Conditional Random Fields
Word Sense Disambiguation for All Words using Tree-Structured Conditional Random Fields
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发表时间：
2008-08
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通讯作者：
Jun Hatori;Yusuke Miyao;Junichi Tsujii
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作者：
Jun Hatori;Yusuke Miyao;Junichi Tsujii
提出了一种基于树结构条件随机场的有监督词义消歧方法。通过将TCRFs应用于描述为依赖树结构的句子，我们将WSD作为树结构上的标记问题进行。为了将词义之间的依赖关系，我们引入了一组功能的树边缘，结合粗粒度的标签集，并表明，这些有助于提高WSD的准确性。我们还证明了树结构模型的性能优于线性链模型。在SENSEVAL-3数据集上的实验表明，我们的TCRF模型可以与最先进的WSD系统进行比较。
We propose a supervised word sense disambiguation (WSD) method using tree-structured conditional random fields (TCRFs). By applying TCRFs to a sentence described as a dependency tree structure, we conduct WSD as a labeling problem on tree structures. To incorporate dependencies between word senses, we introduce a set of features on tree edges, in combination with coarse-grained tagsets, and show that these contribute to an improvement in WSD accuracy. We also show that the tree-structured model outperforms the linear-chain model. Experiments on the SENSEVAL-3 data set show that our TCRF model performs comparably with state-of-the-art WSD systems.