Biomedical Relationship Extraction from Literature Based on Bio-Semantic Token Subsequences

Relationship Extraction (RE) from biomedical literature is an important and challenging problem in both text mining and bioinformatics. Although various approaches have been proposed to extract protein?protein interaction types, their accuracy rates leave a large room for further exploring. In this paper, two supervised learning algorithms based on newly defined bio-semantic token subsequence are proposed for multi-class biomedical relationship classification. The first approach calculates a bio-semantic token subsequence kernel , whereas the second one explicitly extracts weighted features from bio-semantic token subsequences. The two proposed approaches outperform several alternatives reported in literature on multi-class protein?protein interaction classification

Relation-Centric Task Identification for Policy-Based Process Mining

Many organizations use business policies to govern their business processes. For complex business processes, this results in huge amount of policy documents. Given the large volume of policies, manually analyzing policy documents to discover process information imposes excessive cognitive load. In order to provide a solution to this problem, we have proposed previously a novel approach named Policy-based Process Mining (PBPM) to automatically extracting process models from policy documents using information extraction techniques. In this paper, we report our recent findings in an important PBPM step called task identification. Our investigation indicates that task identification from policy documents is quite challenging because it is not a typical information extraction problem. The novelty of our approach is to formalize task identification as a problem of extracting relations among three process components, i.e., resource, action, and data while using sequence kernel techniques. Our initial experiment produced very promising results

Deep learning for extracting protein-protein interactions from biomedical literature

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table

Mean field propagation of Wigner measures and BBGKY hierarchies for general bosonic states

Contrary to the finite dimensional case, Weyl and Wick quantizations are no more asymptotically equivalent in the infinite dimensional bosonic second quantization. Moreover neither the Weyl calculus defined for cylindrical symbols nor the Wick calculus defined for polynomials are preserved by the action of a nonlinear flow. Nevertheless taking advantage carefully of the information brought by these two calculuses in the mean field asymptotics, the propagation of Wigner measures for general states can be proved, extending to the infinite dimensional case a standard result of semiclassical analysis.Comment: 39 page