Many knowledge acquisition tasks are tightly dependent on fundamental analysis technologies, such as part of speech (POS) tagging and parsing. Dependency parsing, in particular, has been widely employed for the acquisition of knowledge related to predicate-argument structures. For such tasks, the dependency parsing performance can determine quality of acquired knowledge, regardless of target languages. Therefore, reducing dependency parsing errors and selecting high quality dependencies is of primary importance. In this study, we present a language-independent approach for automatically selecting high quality dependencies from automatic parses. By considering several aspects that affect the accuracy of dependency parsing, we created a set of features for supervised classification of reliable dependencies. Experimental results on seven languages show that our approach can effectively select high quality dependencies from dependency parses.
This paper presents an application of Chinese syntactic knowledge for semantic role labeling (SRL). Besides basic morphological information, syntactic structures are crucial in SRL. However, it is difficult to learn such information from limited, small-scale, manually annotated training data. Instead of manually increasing the size of annotated data, we use a large amount of automatically extracted syntactic knowledge to improve the performance of SRL.
This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.
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