中文摘要：

在大数据时代，对于海量网络数据的信息抽取与应用已成为自然语言处理和信息检索技术发展的重要主题．其中，基于弱监督的关系抽取方法，因为具有不需要过多人工参与、适应性强的特点，受到了广泛的关注．目前针对它的研究主要集中在英语资源上，主要使用传统的词法和句法特征．然而，词法特征有严重的稀疏性问题，句法特征则对一些语言分析工具的性能有较强的依赖性．提出利用n—gram特征来缓解传统词法特征稀疏性的问题．特别地，这种特征还可以弥补传统句法特征在其他语言上不可靠的情况，对于关系抽取的跨语言应用有重要作用．在此基础上，针对弱监督学中标注数据不完全可靠的情况，提出基于bootstrapping思想的协同训练方法来对弱监督关系抽取模型进行强化，并且对预测关系时的协同策略进行了详细分析．在大规模的中文和英文数据上进行实验的结果显示，把传统特征与n-gram特征相结合并进行协同训练，在中文和英文数据集上均可以提升弱监督关系抽取的效果，可以适应多语言的关系抽取需求．

英文摘要：

In the time of big data, information extraction at a large scale has been an important topic discussed in natural language processing and information retrieval. Specifically, weak supervision, as a novel framework that need not any human involvement and can be easily adapted to new domains, is receiving increasing attentions. The current study of weak supervision is intended primarily for English, with conventional features such as segments of words based lexical features and dependency based syntactic features. However, this type of lexical features often suffer from the data sparsity problem, while syntactic features strongly rely on the availability of syntactic analysis tools. This paper proposes to make use of n-gram features which can relieve to some extent the data sparsity prob mult em brought by lexical features. It is also observed that the n-gram features are important for lingual relation extraction, especially, they can make up for the syntactic features in those languages where syntactic analysis tools are not reliable. In order to deal with the quality issue of training data used in weakly supervised learning models, a bootstrapping approach, co-training, is introduced into the framework to improve this extraction paradigm. We study the strategies used to combine the outputs from different training views. The experimental results on both English and Chinese datasets show that the proposed approach can effectively improve the performance of weak supervision in both languages, and has the potential to work well in a multilingual scenario with more languages.