Proteins and their interactions govern virtually all cellular processes, such as 
regulation, signaling, metabolism, and structure. Most experimental findings 
pertaining to such interactions are discussed in research papers, which, in 
turn, get curated by protein interaction databases. Authors, editors, and 
publishers benefit from efforts to alleviate the tasks of searching for relevant 
papers, evidence for physical interactions, and proper identifiers for each 
protein involved. The BioCreative II.5 community challenge addressed these tasks 
in a competition-style assessment to evaluate and compare different 
methodologies, to make aware of the increasing accuracy of automated methods, 
and to guide future implementations. In this paper, we present our approaches 
for protein-named entity recognition, including normalization, and for 
extraction of protein-protein interactions from full text. Our overall goal is 
to identify efficient individual components, and we compare various compositions 
to handle a single full-text article in between 10 seconds and 2 minutes. We 
propose strategies to transfer document-level annotations to the sentence-level, 
which allows for the creation of a more fine-grained training corpus; we use 
this corpus to automatically derive around 5,000 patterns. We rank sentences by 
relevance to the task of finding novel interactions with physical evidence, 
using a sentence classifier built from this training corpus. Heuristics for 
paraphrasing sentences help to further remove unnecessary information that might 
interfere with patterns, such as additional adjectives, clauses, or bracketed 
expressions. In BioCreative II.5, we achieved an f-score of 22 percent for 
finding protein interactions, and 43 percent for mapping proteins to UniProt 
IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. 
On average, our best-performing setup required around 2 minutes per full text. 
All data and pattern sets as well as Java classes that extend- - third-party 
software are available as supplementary information (see Appendix).