99 research outputs found
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, “off-the-page ” representation and evaluation to complement the mentionlevel evaluations pursued in most recent work.
Boosting automatic event extraction from the literature using domain adaptation and coreference resolution
Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage
Coreference based event-argument relation extraction on biomedical text
This paper presents a new approach to exploit coreference information for extracting event-argument (E-A) relations from biomedical documents. This approach has two advantages: (1) it can extract a large number of valuable E-A relations based on the concept of salience in discourse; (2) it enables us to identify E-A relations over sentence boundaries (cross-links) using transitivity of coreference relations. We propose two coreference-based models: a pipeline based on Support Vector Machine (SVM) classifiers, and a joint Markov Logic Network (MLN). We show the effectiveness of these models on a biomedical event corpus. Both models outperform the systems that do not use coreference information. When the two proposed models are compared to each other, joint MLN outperforms pipeline SVM with gold coreference information
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 “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.Siirretty Doriast
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