Evolving rules for document classification

We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications

Evolving text classification rules with genetic programming

We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that the rules may have a number of other uses beyond classification and provide a basis for text mining applications

ON MONITORING LANGUAGE CHANGE WITH THE SUPPORT OF CORPUS PROCESSING

One of the fundamental characteristics of language is that it can change over time. One method to monitor the change is by observing its corpora: a structured language documentation. Recent development in technology, especially in the field of Natural Language Processing allows robust linguistic processing, which support the description of diverse historical changes of the corpora. The interference of human linguist is inevitable as it determines the gold standard, but computer assistance provides considerable support by incorporating computational approach in exploring the corpora, especially historical corpora. This paper proposes a model for corpus development, where corpus are annotated to support further computational operations such as lexicogrammatical pattern matching, automatic retrieval and extraction. The corpus processing operations are performed by local grammar based corpus processing software on a contemporary Indonesian corpus. This paper concludes that data collection and data processing in a corpus are equally crucial importance to monitor language change, and none can be set aside