Data-mining parallel passages

JOURNAL OF DATA MINING AND DIGITAL HUMANITIES:

Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus

Avi Shmidman1, Moshe Koppel2, Ely Porat3

1 Department of Hebrew Literature, Bar-Ilan University, Israel, and Dicta: The Israel Center for Text Analysis

2 Department of Computer Science, Bar-Ilan University, Israel, and Dicta: The Israel Center for Text Analysis

3 Department of Computer Science, Bar-Ilan University, Israel

Abstract
We propose a method for efficiently finding all parallel passages in a large corpus, even if the passages are not quite identical due to rephrasing and orthographic variation. The key ideas are the representation of each word in the corpus by its two most infrequent letters, finding matched pairs of strings of four or five words that differ by at most one word and then identifying clusters of such matched pairs. Using this method, over 4600 parallel pairs of passages were identified in the Babylonian Talmud, a Hebrew-Aramaic corpus of over 1.8 million words, in just over 30 seconds. Empirical comparisons on sample data indicate that the coverage obtained by our method is essentially the same as that obtained using slow exhaustive methods.

Keywords
approximate matching; fuzzy matching; text reuse

HT the Talmud Blog on Facebook. Oddly, I can find no listing of this article on the journal's website.