7 research outputs found
MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input
text to assist the reader in gaining a quick yet informative gist. Existing
text summarization methods generally focus on a single aspect of text when
selecting sentences, causing the potential loss of essential information. In
this study, we propose a domain-specific method that models a document as a
multi-layer graph to enable multiple features of the text to be processed at
the same time. The features we used in this paper are word similarity, semantic
similarity, and co-reference similarity, which are modelled as three different
layers. The unsupervised method selects sentences from the multi-layer graph
based on the MultiRank algorithm and the number of concepts. The proposed
MultiGBS algorithm employs UMLS and extracts the concepts and relationships
using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation
by ROUGE and BERTScore shows increased F-measure values
Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm
Background: Shared tasks and community challenges represent key instruments to promote research, collaboration
and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such tasks
relied on the comparison of automatically generated results against a so-called Gold Standard dataset of manually
labelled textual data, regardless of efficiency and robustness of the underlying implementations. Due to the rapid
growth of unstructured data collections, including patent databases and particularly the scientific literature, there is a
pressing need to generate, assess and expose robust big data text mining solutions to semantically enrich documents
in real time. To address this pressing need, a novel track called “Technical interoperability and performance of annotation
servers” was launched under the umbrella of the BioCreative text mining evaluation effort. The aim of this track
was to enable the continuous assessment of technical aspects of text annotation web servers, specifically of online
biomedical named entity recognition systems of interest for medicinal chemistry applications.
Results: A total of 15 out of 26 registered teams successfully implemented online annotation servers. They returned
predictions during a two-month period in predefined formats and were evaluated through the BeCalm evaluation
platform, specifically developed for this track. The track encompassed three levels of evaluation, i.e. data format
considerations, technical metrics and functional specifications. Participating annotation servers were implemented
in seven different programming languages and covered 12 general entity types. The continuous evaluation of server
responses accounted for testing periods of low activity and moderate to high activity, encompassing overall 4,092,502
requests from three different document provider settings. The median response time was below 3.74 s, with a median
of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process over
100,000 requests in a 5-day period.
Conclusions: The presented track was a novel experimental task that systematically evaluated the technical performance
aspects of online entity recognition systems. It raised the interest of a significant number of participants.
Future editions of the competition will address the ability to process documents in bulk as well as to annotate full-text
documents.Portuguese Foundation for Science and Technology | Ref. UID/BIO/04469/2013Portuguese Foundation for Science and Technology | Ref. COMPETE 2020 (POCI-01-0145-FEDER-006684)Xunta de Galicia | Ref. ED431C2018/55-GRCEuropean Commission | Ref. H2020, n. 65402
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
publishedVersionPeer reviewe
OGER++: hybrid multi-type entity recognition
Abstract Background We present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to the previous step. Results We evaluated the system in terms of processing speed and annotation quality. In the speed benchmarks, the OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second. On the CRAFT corpus, we achieved 71.4% and 56.7%Â F1 for named entity recognition and concept recognition, respectively. Conclusions Combining knowledge-based and data-driven components allows creating a system with competitive performance in biomedical text mining
OGER++: hybrid multi-type entity recognition
Background: We present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to the previous step.
Results: We evaluated the system in terms of processing speed and annotation quality. In the speed benchmarks, the OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second. On the CRAFT corpus, we achieved 71.4% and 56.7% F1 for named entity recognition and concept recognition, respectively.
Conclusions: Combining knowledge-based and data-driven components allows creating a system with competitive performance in biomedical text mining