Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.
: assumes that if two entities are mentioned in the same sentence (co-occur), it is likely that they are related. Usually, the application of this approach results in a higher recall (most of the entities co-occurring in a sentence participate in a relation), and lower precision. Some methods use frequency-based scoring schemes to eliminate relations identified by chance @cite_47 . Nowadays, most applications use co-occurrence as a baseline against more complex approaches @cite_45 .
@cite_47 @cite_45
2099369363 2611774767
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year. PCT No. PCT NL83 00020 Sec. 371 Date Jan. 17, 1984 Sec. 102(e) Date Jan. 17, 1984 PCT Filed May 19, 1983 PCT Pub. No. WO83 04106 PCT Pub. Date Nov. 24, 1983.Method and apparatus for the measurement of fluid flow velocities for instance in a slurry transport conduit, in which pulse signals are sent through the fluid by pairs of transmitting and receiving transducers, the pulses having such time separation that standing waves caused by reflection fade out within the time separation, and using side band techniques and correlation techniques.