87 research outputs found
BioNLP Shared Task - The Bacteria Track
Background: We present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming supporting task is aimed at extracting gene renaming and gene name synonymy in PubMed abstracts. The Bacteria Gene Interaction is a gene/protein interaction extraction task from individual sentences. The interactions have been categorized into ten different sub-types, thus giving a detailed account of genetic regulations at the molecular level. Finally, the Bacteria Biotopes task focuses on the localization and environment of bacteria mentioned in textbook articles. We describe the process of creation for the three corpora, including document acquisition and manual annotation, as well as the metrics used to evaluate the participants' submissions. Results: Three teams submitted to the Bacteria Gene Renaming task; the best team achieved an F-score of 87%. For the Bacteria Gene Interaction task, the only participant's score had reached a global F-score of 77%, although the system efficiency varies significantly from one sub-type to another. Three teams submitted to the Bacteria Biotopes task with very different approaches; the best team achieved an F-score of 45%. However, the detailed study of the participating systems efficiency reveals the strengths and weaknesses of each participating system. Conclusions: The three tasks of the Bacteria Track offer participants a chance to address a wide range of issues in Information Extraction, including entity recognition, semantic typing and coreference resolution. We found commond trends in the most efficient systems: the systematic use of syntactic dependencies and machine learning. Nevertheless, the originality of the Bacteria Biotopes task encouraged the use of interesting novel methods and techniques, such as term compositionality, scopes wider than the sentence
New Resources and Perspectives for Biomedical Event Extraction
Event extraction is a major focus of recent work in biomedical information extraction. Despite substantial advances, many challenges still remain for reliable automatic extraction of events from text. We introduce a new biomedical event extraction resource consisting of analyses automatically created by systems participating in the recent BioNLP Shared Task (ST) 2011. In providing for the first time the outputs of a broad set of state-ofthe-art event extraction systems, this resource opens many new opportunities for studying aspects of event extraction, from the identification of common errors to the study of effective approaches to combining the strengths of systems. We demonstrate these opportunities through a multi-system analysis on three BioNLP ST 2011 main tasks, focusing on events that none of the systems can successfully extract. We further argue for new perspectives to the performance evaluation of domain event extraction systems, considering a document-level, âoff-the-page â representation and evaluation to complement the mentionlevel evaluations pursued in most recent work.
Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity
Microbiology research has access to a very large amount of public information
on the habitats of microorganisms. Many areas of microbiology research uses
this information, primarily in biodiversity studies. However the habitat
information is expressed in unstructured natural language form, which hinders
its exploitation at large-scale. It is very common for similar habitats to be
described by different terms, which makes them hard to compare automatically,
e.g. intestine and gut. The use of a common reference to standardize these
habitat descriptions as claimed by (Ivana et al., 2010) is a necessity. We
propose the ontology called OntoBiotope that we have been developing since
2010. The OntoBiotope ontology is in a formal machine-readable representation
that enables indexing of information as well as conceptualization and
reasoning.Comment: 5 page
BioRED: A Comprehensive Biomedical Relation Extraction Dataset
Automated relation extraction (RE) from biomedical literature is critical for
many downstream text mining applications in both research and real-world
settings. However, most existing benchmarking datasets for bio-medical RE only
focus on relations of a single type (e.g., protein-protein interactions) at the
sentence level, greatly limiting the development of RE systems in biomedicine.
In this work, we first review commonly used named entity recognition (NER) and
RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus
with multiple entity types (e.g., gene/protein, disease, chemical) and relation
pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles.
Further, we label each relation as describing either a novel finding or
previously known background knowledge, enabling automated algorithms to
differentiate between novel and background information. We assess the utility
of BioRED by benchmarking several existing state-of-the-art methods, including
BERT-based models, on the NER and RE tasks. Our results show that while
existing approaches can reach high performance on the NER task (F-score of
89.3%), there is much room for improvement for the RE task, especially when
extracting novel relations (F-score of 47.7%). Our experiments also demonstrate
that such a comprehensive dataset can successfully facilitate the development
of more accurate, efficient, and robust RE systems for biomedicine
Optimizing text mining methods for improving biomedical natural language processing
The overwhelming amount and the increasing rate of publication in the biomedical domain make it difficult for life sciences researchers to acquire and maintain all information that is necessary for their research. Pubmed (the primary citation database for the biomedical literature) currently contains over 21 million article abstracts and more than one million of them were published in 2020 alone.
Even though existing article databases provide capable keyword search services, typical everyday-life queries usually return thousands of relevant articles. For instance, a cancer research scientist may need to acquire a complete list of genes that interact with BRCA1 (breast cancer 1) gene. The PubMed keyword search for BRCA1 returns over 16,500 article abstracts, making manual inspection of the retrieved documents impractical. Missing even one of the interacting gene partners in this scenario may jeopardize successful development of a potential new drug or vaccine. Although manually curated databases of biomolecular interactions exist, they are usually not up-to-date and they require notable human effort to maintain. To summarize, new discoveries are constantly being shared within the community via scientific publishing, but unfortunately the probability of missing vital information for research in life sciences is increasing.
In response to this problem, the biomedical natural language processing (BioNLP) community of researchers has emerged and strives to assist life sciences researchers by building modern language processing and text mining tools that can be applied at large-scale and scan the whole publicly available literature and extract, classify, and aggregate the information found within, thus keeping life sciences researchers always up-to-date with the recent relevant discoveries and facilitating their research in numerous fields such as molecular biology, biomedical engineering, bioinformatics, genetics engineering and biochemistry.
My research has almost exclusively focused on biomedical relation and event extraction tasks. These foundational information extraction tasks deal with automatic detection of biological processes, interactions and relations described in the biomedical literature. Precisely speaking, biomedical relation and event extraction systems can scan through a vast amount of biomedical texts and automatically detect and extract the semantic relations of biomedical named entities (e.g. genes, proteins, chemical compounds, and diseases). The structured outputs of such systems (i.e., the extracted relations or events) can be stored as relational databases or molecular interaction networks which can easily be queried, filtered, analyzed, visualized and integrated with other structured data sources. Extracting biomolecular interactions has always been the primary interest of BioNLP researcher because having knowledge about such interactions is crucially important in various research areas including precision medicine, drug discovery, drug repurposing, hypothesis generation, construction and curation of signaling pathways, and protein function and structure prediction.
State-of-the-art relation and event extraction methods are based on supervised machine learning, requiring manually annotated data for training. Manual annotation for the biomedical domain requires domain expertise and it is time-consuming.
Hence, having minimal training data for building information extraction systems is a common case in the biomedical domain. This demands development of methods that can make the most out of available training data and this thesis gathers all my research efforts and contributions in that direction.
It is worth mentioning that biomedical natural language processing has undergone a revolution since I started my research in this field almost ten years ago. As a member of the BioNLP community, I have witnessed the emergence, improvementâ and in some cases, the disappearanceâof many methods, each pushing the performance of the best previous method one step further. I can broadly divide the last ten years into three periods. Once I started my research, feature-based methods that relied on heavy feature engineering were dominant and popular. Then, significant advancements in the hardware technology, as well as several breakthroughs in the algorithms and methods enabled machine learning practitioners to seriously utilize artificial neural networks for real-world applications. In this period, convolutional, recurrent, and attention-based neural network models became dominant and superior. Finally, the introduction of transformer-based language representation models such as BERT and GPT impacted the field and resulted in unprecedented performance improvements on many data sets. When reading this thesis, I demand the reader to take into account the course of history and judge the methods and results based on what could have been done in that particular period of the history
Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach
International audienceBackground: Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field
Biomedical Event Extraction with Machine Learning
Biomedical natural language processing (BioNLP) is a subfield of natural
language processing, an area of computational linguistics concerned
with developing programs that work with natural language: written texts and
speech. Biomedical relation extraction concerns the detection of
semantic relations such as protein--protein interactions (PPI) from scientific
texts. The aim is to enhance information retrieval by detecting relations
between concepts, not just individual concepts as with a keyword search.
In recent years, events have been proposed as a more detailed alternative for
simple pairwise PPI relations. Events provide a systematic, structural
representation for annotating the content of natural language texts. Events are
characterized by annotated trigger words, directed and typed arguments and the
ability to nest other events. For example, the sentence ``Protein A causes
protein B to bind protein C'' can be annotated with the nested event structure
CAUSE(A, BIND(B, C)). Converted to such formal representations, the
information of natural language texts can be used by computational
applications. Biomedical event annotations were introduced by the BioInfer and
GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task
on Event Extraction.
In this thesis we present a method for automated event extraction, implemented
as the Turku Event Extraction System (TEES). A unified graph format is defined
for representing event annotations and the problem of extracting complex event
structures is decomposed into a number of independent classification tasks.
These classification tasks are solved using SVM and RLS classifiers, utilizing
rich feature representations built from full dependency parsing. Building on
earlier work on pairwise relation extraction and using a generalized graph
representation, the resulting TEES system is capable of detecting binary
relations as well as complex event structures.
We show that this event extraction system has good performance,
reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently,
TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared
Tasks, as well as shown competitive performance in the binary relation Drug-Drug
Interaction Extraction 2011 and 2013 shared tasks.
The Turku Event Extraction System is published as a freely available open-source
project, documenting the research in detail as well as making the method
available for practical applications. In particular, in this thesis we
describe the application of the event extraction method to PubMed-scale text
mining, showing how the developed approach not only shows good performance, but
is generalizable and applicable to large-scale real-world text mining projects.
Finally, we discuss related literature, summarize the contributions of the work
and present some thoughts on future directions for biomedical event extraction.
This thesis includes and builds on six original research publications. The first
of these introduces the analysis of dependency parses that leads to
development of TEES. The entries in the three BioNLP Shared Tasks, as well as
in the DDIExtraction 2011 task are covered in four publications, and the sixth
one demonstrates the application of the system to PubMed-scale text mining.</p
Biomedical Event Extraction with Machine Learning
Biomedical natural language processing (BioNLP) is a subfield of natural
language processing, an area of computational linguistics concerned with
developing programs that work with natural language: written texts and
speech. Biomedical relation extraction concerns the detection of semantic
relations such as protein-protein interactions (PPI) from scientific texts.
The aim is to enhance information retrieval by detecting relations between
concepts, not just individual concepts as with a keyword search.
In recent years, events have been proposed as a more detailed alternative
for simple pairwise PPI relations. Events provide a systematic, structural
representation for annotating the content of natural language texts. Events
are characterized by annotated trigger words, directed and typed arguments
and the ability to nest other events. For example, the sentence âProtein A
causes protein B to bind protein Câ can be annotated with the nested event
structure CAUSE(A, BIND(B, C)). Converted to such formal representations,
the information of natural language texts can be used by computational
applications. Biomedical event annotations were introduced by the
BioInfer and GENIA corpora, and event extraction was popularized by the
BioNLP'09 Shared Task on Event Extraction.
In this thesis we present a method for automated event extraction, implemented
as the Turku Event Extraction System (TEES). A unified graph
format is defined for representing event annotations and the problem of
extracting complex event structures is decomposed into a number of independent
classification tasks. These classification tasks are solved using SVM
and RLS classifiers, utilizing rich feature representations built from full dependency
parsing. Building on earlier work on pairwise relation extraction
and using a generalized graph representation, the resulting TEES system is
capable of detecting binary relations as well as complex event structures.
We show that this event extraction system has good performance, reaching
the first place in the BioNLP'09 Shared Task on Event Extraction.
Subsequently, TEES has achieved several first ranks in the BioNLP'11 and
BioNLP'13 Shared Tasks, as well as shown competitive performance in the
binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared
tasks.
The Turku Event Extraction System is published as a freely available
open-source project, documenting the research in detail as well as making
the method available for practical applications. In particular, in this thesis
we describe the application of the event extraction method to PubMed-scale
text mining, showing how the developed approach not only shows good
performance, but is generalizable and applicable to large-scale real-world
text mining projects.
Finally, we discuss related literature, summarize the contributions of the
work and present some thoughts on future directions for biomedical event
extraction. This thesis includes and builds on six original research publications.
The first of these introduces the analysis of dependency parses that
leads to development of TEES. The entries in the three BioNLP Shared
Tasks, as well as in the DDIExtraction 2011 task are covered in four publications,
and the sixth one demonstrates the application of the system to
PubMed-scale text mining.Siirretty Doriast
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