KXtractor: An effective biomedical information extraction technique based on mixture hidden markov models

We present a novel information extraction (IE) technique, KXtractor, which combines a text chunking technique and Mixture Hidden Markov Models (MiHMM). KXtractor overcomes the problem of the single Part-Of-Speech (POS) HMMs with modeling the rich representation of text where features overlap among state units such as word, line, sentence, and paragraph. KXtractor also resolves issues with the traditional HMMs for IE that operate only on the semi-structured data such as HTML documents and other text sources in which language grammar does not play a pivotal role. We compared KXtractor with three IE techniques: 1) RAPIER, an inductive learning-based machine learning system, 2) a Dictionary-based extraction system, and 3) single POS HMM. Our experiments showed that KXtractor outperforms these three IE systems in extracting protein-protein interactions. In our experiments, the F-measure for KXtractor was higher than for RAPIER, a dictionary-based system, and single POS HMM respectively by 16.89%, 16.28%, and 8.58%. In addition, both precision and recall of KXtractor are higher than those systems.