Haibo Li

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Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted(More)
Binary semantic relation extraction from Wikipedia is particularly useful for various NLP and Web applications. Currently frequent pattern mining-based methods and syntactic analysis-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating syntactic analysis on Wikipedia text with redundancy(More)
— Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Web-based methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view(More)
Many tasks of information extraction or natural language processing have a property that the data naturally consist of several views—disjoint subsets of features. Specifically, a semantic relationship can be represented with some entity pairs or contexts surrounding the entity pairs. For example, the Person-Birthplace relation can be recognized from the(More)
In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based(More)
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