Corpus-Induced Corpus Clean-up

We explore the feasibility of using only unsupervised means to identify non-words, i.e. typos, in a frequency list derived from a large corpus of Dutch and to distinguish between these non-words and real-words in the language. We call the system we built and evaluate in this paper CICCL, which stands for ‘Corpus-Induced Corpus Clean-up’. The algorithm on which CICCL is primarily based is the anagram-key hashing algorithm introduced by (Reynaert, 2004). The core correction mechanism is a simple and effective method which translates the actual characters which make up a word into a large natural number in such a way that all the anagrams, i.e. all the words composed of precisely the same subset of characters, are allocated the same natural number. In effect, this constitutes a novel approximate string matching algorithm for indexed text search. This is because by simple addition, subtraction or a combination of both, all variants within reach of the range of numerical values defined in the alphabet are retrieved by iterating over the alphabet. CICCL’s input consists primarily of corpus derived frequency lists, from which it derives valuable morphological and compounding information by performing frequency counts over the substrings of the words. These counts are then used to perform decompounding, as well as for distinguishing between most likely correctly spelled words and typos.