Efficient Key-Substring Group Feature Extraction for Enhanced Text Classification
This paper presents an innovative key-substring group feature extraction technique aimed at addressing the challenges of effectiveness and efficiency in text classification using string kernels. We introduce a suffix tree-based algorithm that enables the extraction of features in linear time, significantly improving the handling of large substring corpora. Our methodology and experimental results demonstrate high accuracy rates in various text classification tasks, including English and Chinese topic classification (87.3%) and Greek authorship classification (92%). This approach opens new avenues for further research and application.
Efficient Key-Substring Group Feature Extraction for Enhanced Text Classification
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Presentation Transcript
Extracting key-substring-group features for text classification Advisor : Dr. Hsu Graduate : Chen, Shao-Pei Authors : Dell Zhang, Wee Sun Lee 2006.SIGKDD.10
Outline • Motivation • Objective • Methodology • Experimental Results • Conclusion
The huge number of substrings in the training corpus D is an obstacle to making use of discriminative learning methods for text classification. A document of length |d| has at least |d|(|d|+1)/2 matches of substring with itself. Motivation abc
We proposed key-substring-group feature extraction technique solves the effectiveness and efficiency problems of string kernel, and opens numerous promising directions. We proposed a suffix tree based algorithm that can extract such features in linear time. Objective abc bc c
Methodology-suffix tree 1xabxa$ 2abxa$ 3bxa$ 4xa$ 5a$ 6$ 1xabxa$ 4xa$ 2abxa$ 5a$ 3bxa$ 6$ Theorem 2. Given an arbitrary string P, we can find all occurrences of P in S in O(|P|) time taking advantage of the suffix tree T for S. Theorem 3. If P is a substring of S, the occurrence frequency of P in S would be equal to the number of leaves in the subtree of T rooted at P. Theorem 5. Ukkonen’s algorithm can construct the suffix tree T for a string S of length n, along with all its suffix links, in O(n) time. Theorem 8. Suppose the corpus is of size n . Then there are n trivial groups whose substrings occur only once in D, and at most n-1 non-trivial groups. 5
Methodology-algorithm l: the minimum frequency. h: the maximum frequency. b: the minimum number of branches (children). p: the maximum parent-child conditional probability. q: the maximum suffix-link conditional probability. 6
Experimental Results l, h, b, p and q Initially we took the default values for those parameters which would not filter any nontrivial substring-group out. Then we reduced the number of features gradually by adjusting the parameters. English Text Topic Classification Chinese Text Topic Classification:87.3% Greek Text Authorship Classification:92% Greek Text Genre Classification:94%
Conclusion Our proposed key-substring-group feature extraction technique solves the effectiveness and efficiency problems of string kernel, and opens numerous promising directions.
