A detailed error analysis of 13 kernel methods for protein-protein interaction extraction.

Background: Kernel-based classification is the current state-of-the-art for extracting pairs of interacting proteins (PPIs) from free text. Various proposals have been put forward, which diverge especially in the specific kernel function, the type of input representation, and the feature sets. These proposals are regularly compared to each other regarding their overall performance on different gold standard corpora, but little is known about their respective performance on the instance level.

Simple tricks for improving pattern-based information extraction from the biomedical literature.

Background: Pattern-based approaches to relation extraction have shown very good results in many areas of biomedical text mining. However, defining the right set of patterns is difficult; approaches are either manual, incurring high cost, or automatic, often resulting in large sets of noisy patterns.

Results: We propose several techniques for filtering sets of automatically generated patterns and analyze their effectiveness for different extraction tasks, as defined in the recent BioNLP 2009 shared task.

Improving textual medication extraction using combined conditional random fields and rule-based systems.

Objective: In the i2b2 Medication Extraction Challenge, medication names together with details of their administration were to be extracted from medical discharge summaries.

Design: The task of the challenge was decomposed into three pipelined components: named entity identification, context-aware filtering and relation extraction. For named entity identification, first a rule-based (RB) method that was used in our overall fifth place-ranked solution at the challenge was investigated.

A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences.

Semantic classification of diseases in discharge summaries using a context-aware rule-based classifier.

OBJECTIVE Automated and disease-specific classification of textual clinical discharge summaries is of great importance in human life science, as it helps physicians to make medical studies by providing statistically relevant data for analysis. This can be further facilitated if, at the labeling of discharge summaries, semantic labels are also extracted from text, such as whether a given disease is present, absent, questionable in a patient, or is unmentioned in the document. The authors present a classification technique that successfully solves the semantic classification task.