A Rich Feature Vector for Protein Protein Interaction Extraction from Multiple Corpora
Because of the importance of protein protein interaction (PPI) extraction from text, many corpora have been proposed with slightly differing definitions of proteins and PPI. Since no single corpus is large enough to saturate a machine learning system, it is necessary to learn from multiple different corpora. In this paper, we propose a solution to this challenge. We designed a rich feature vector, and we applied a support vector machine modified for corpus weighting (SVM CW) to complete the task of multiple corpora PPI extraction. The rich feature vector, made from multiple useful kernels, is used to express the important information for PPI extraction, and the system with our feature vector was shown to be both faster and more accurate than the original kernel based system, even when using just a single corpus. SVM CW learns from one corpus, while using other corpora for support. SVM CW is simple, but it is more effective than other methods that have been successfully applied to other NLP tasks earlier. With the feature vector and SVM CW, our system achieved the best performance among all state of the art PPI extraction systems reported so far.
EMNLP