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A wealth of valuable research data is locked within the millions of research articles published every year. Reading and extracting pertinent information from those articles has become an unmanageable task for scientists. Moreover, these data are loosely structured, encoded in manuscripts of various formats, embedded in different content types, and are, in general, not machine accessible. Thus, studies that automatically leverage this valuable information are not tractable or even possible. Current approaches employ humans to manually extract data, define extraction rules, or annotate training corpora for machine learning approaches through tedious, time-consuming, error-prone and sometimes expensive processes. In the specific case of scientific information extraction, the need for pointed expertise increases costs and decreases the generalization of extraction methods. This thesis seeks to demonstrate that efficient combination of human-computer extraction techniques can considerably alleviate the burden on human curators, thereby speeding up discovery of new scientific facts and decreasing extraction costs. This thesis is investigated in the context of materials informatics, an emerging field that has the potential to greatly reduce time-to-market and development costs for new materials. Such efforts rely on access to large databases of material properties and therefore represent a suitable but not unique application for this research. This work addresses the challenge of populating a database of scientific facts by presenting three approaches with different levels of automation and human involvement. Specifically, these three approaches involve varying amount of untrained, trained and expert input in order to populate a database of polymer properties. The first effort, 𝛘DB, engages a semi-expert crowd to extract an important relation in polymer science. Here automation is limited, being concerned only with identifying appropriate elements of scientific articles to present to crowd members. However, the approach is shown to accelerate data extraction speed considerably. 𝛘DB is a crowdsourcing system, which employs and assists a semi-expert crowd to extract an important relation in polymer science. Increasing the automation and targeting a different relation, the Tg approach is a pipeline that uses a variety of computer and human modules or tasks to supplement the output of a well-performing natural language processing software and prioritize expert curation. Having identified, named scientific named entity recognition as a major challenge and prerequisite for relations extraction, polyNER, the third approach uses minimal, focused expert knowledge to generate annotated entity-rich corpora data and bootstrap scientific named entities classifiers. This work shows that systems combining existing software and minimal human input can achieve performance comparable to that of a state-of-the-art domain-specific Natural Language Processing software and demonstrates the potential of hybrid human-computer partnership alternatives to sometimes impractical state-of-the-art approaches.




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