Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
WordNet: a lexical database for English
Communications of the ACM
Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Journal of Intelligent Information Systems
Automated scoring using a hybrid feature identification technique
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Latent Semantic Kernels for WordNet: Transforming a Tree-Like Structure into a Matrix
ALPIT '08 Proceedings of the 2008 International Conference on Advanced Language Processing and Web Information Technology
Automatic short answer marking
EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
An intelligent grading system using heterogeneous linguistic resources
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Hi-index | 12.05 |
In this paper, we develop an autonomous assessment system based on the kernel combinations which are mixed by two kernel matrices from the WordNet and corpus. Many researchers have tried to integrate these two resources in many applications, to utilize diverse information extracted from each resource. However, since two resources have been represented in quite different ways, one resource has been secondary to another. To fully integrate two resources at the same level, we first transform the WordNet, which has a hierarchical structure, into a matrix structure. Concurrently, another matrix, which represents a co-occurrence of words in the collection of text documents, is constructed. We then build two initial latent semantic kernels from both matrices and merge them into a new single kernel matrix. When we merge two matrices, we split each initial matrix into independent columns and mix the columns with various methods. We acquire a few combined kernel matrices which show various performances in experiments. Compared to the basic vector space model, original kernel matrices, and the BLEU based method, the combined matrices improve the accuracy of assessment.