面向特定领域应用的向量空间模型融合
Fusing Vector Space Models for Domain-Specific Applications
摘要：
我们解决了针对特定用例和领域调优词嵌入的问题。我们提出了一种新的方法，自动组合多个特定领域的嵌入，从广泛的预训练特定领域的嵌入，以提高其组合的表达能力。我们的方法依赖于两个关键组件：1）基于新的嵌入相似性度量的排名函数，该函数选择最相关的嵌入以使用给定的域; 2）降维方法，该方法结合所选嵌入以产生更紧凑和更有效的编码，从而保留表现力。我们的经验表明，我们的方法产生了有效的特定领域的嵌入，不断提高性能的国家的最先进的机器学习算法在多个任务，与通用嵌入大型文本语料库训练。
英文摘要：
We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to improve their combined expressive power. Our approach relies on two key components: 1) a ranking function, based on a new embedding similarity measure, that selects the most relevant embeddings to use given a domain and 2) a dimensionality reduction method that combines the selected embeddings to produce a more compact and efficient encoding that preserves the expressiveness. We empirically show that our method produces effective domain-specific embeddings that consistently improve the performance of state-of-the-art machine learning algorithms on multiple tasks, compared to generic embeddings trained on large text corpora.
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。
