Yuqian Wu

Named Entity Resolution in Chinese News Comments on the Web

News comment is a new text genre which people use to express their opinions on recent news events. Different from normal text corpus, news comments have some particular properties. The named entities in the news comments usually use some wrongly written words, informal abbreviations or aliases, which bring great difficulties for machine detection and understanding. This paper addresses the issue of named entity resolution in Chinese news comments on the web, which is a special case of coreference resolution. Traditional resolution algorithms have some limitations for this special task. In this paper, we first define the special task, and then propose a novel resolution algorithm with new features to improve the resolution performance. We manually labeled a benchmark dataset with 60 pieces of news and their corresponding comments downloaded from a popular Chinese news portal and the experimental results on the dataset show that our algorithm is effective for this special task.

Integrating feature ranking and clustering method to discover person relations in web news

Extracting the social relation network of persons is challenging. Discovering significant binary person relations embedded in the web news would be appropriate as the starting point. Prior methods for this task, however, chose to define the relation types first, focused on a few limited types, and always took over a large amount of web information. This paper describes an unsupervised person relation extraction system. This system automatically extracts important people relations from a limited batch of web news, and then proceeds to cluster the instances of these relations and finds discriminative words to represent different clusters. We use various feature ranking strategies for filtering instead of simple bag-of-words representation. We present the experiments evaluation results and give an overview of possible enhancements of this system.