形态森林的无监督学习
Unsupervised Learning of Morphological Forests
摘要：
本文重点研究形态家族的无监督建模，这些家族共同组成了语言词汇的森林。这个公式使我们能够捕捉反映单步形态衍生的沿边属性，以及整个森林的全球分布特性。这些全局属性限制了词缀集的大小，并促进了紧密形态家族的形成。由此产生的目标是解决使用双线性规划（ILP）与对比估计配对。我们通过交替优化局部对数线性模型和全局ILP目标来训练模型。我们评估我们的系统上的三个任务：根检测，聚类的形态家族和分割。我们的实验表明，我们的模型产生一致的收益，在所有三个任务相比，最好的公布的结果。
英文摘要：
This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edgewise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families and segmentation. Our experiments demonstrate that our model yields consistent gains in all three tasks compared with the best published results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
