Software suite for advanced corpus processing
Generic software suite and middle-ware for SNLP. UNIX philosophy: a building kit of small units that may be combined to a new toolchain. Language-agnostic. Written in Perl, automated testsuite of high code coverage, UTF-8 aware. CLI based, Efficient & parallel processing. Thorough documentation. Interactive mode available.
A categorization of 250 parallel texts (sourced in European Medical Agency - EMA) in 20 European languages was performed The resulting graphs (trees) show similarities in their structure.
The general task for the Text Categorization app is to categorize various documents in any language. A great care was taken on following features: High modularity. High performance. Support for parallel processing. The modularity of the source code allows the user to change the behaviour of all procedural steps. The whole application is extensible by simple plugins. Areas of possible application: language identifcation, corpus sorting, forensic linguistics and others.
The categorization process consists of several steps: Extraction of text from all given documents. Filtering of unwanted documents according to given cri-teria (completely modular). Computation of a vector for any document (completely modular). Computation of the distances (completely modular). Hierarchical agglomerative clustering. The final method depends on deployed module. Visualize a binary tree representing relations among the texts (dendrogram).
Vector: a list of values that characterize diferences among texts. Example of 4-elements vector:
1. frequencies of word occurrences
2. average words count in sentence
3. average words length
4. type - token ratio
This will need 4 simple plugins, each computing one of vectors. Each vector can have diferent weight in the computation of the distance. The clustering starts after measuring of distances.
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