In order to improve the performance of frame disambiguation model, this paper used a neural network model to learn frame representation based on sentences in corpus different from the traditional classification algorithm extracting features manually, and employed the learned frame representation on the frame disambiguation task, which significantly improved the performance of the task. Making full use of the CFN example sentence database and being based on the hinge-loss neural network, the algorithm learnt the frame representation that could distinguish the correct frame from the error frame in the largest degree. This paper also used the WSABIE algorithm to learn the representation vector of the context of the target word, and finally used the cosine distance between the context representation vector and the frame representation vector to make a decision for the task. Experiment performed three sets of two-fold cross-validation(3×2 BCV) on 88 ambiguous words in CFN, and the best accuracy of frame disambiguation reach to 72.52%. The t-test results show that the performance of the proposed method is significantly higher than other frame disambiguation methods.

[2020-12-05] Printed Article /doc/2019.09.0535/v2