Event extraction across multiple levels of biological organization

Pyysalo, Sampo; Ohta, Tomoko; Misawa, Miwa; Cho, Han-Cheol; Tsujii, Jun’ichi; Ananiadou, Sophia

doi:10.1093/bioinformatics/bts407

Cited by 121 publications

(126 citation statements)

References 32 publications

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“…mTOR consists of abstracts referenced as evidence for reactions curated in the mTOR signalling pathway [25], annotated for entities and events relevant to the pathway model formalism [7]. Multi-Level Event Extraction (MLEE) consists of abstracts in the blood vessel development subdomain that have been annotated using a comprehensive set of entity and event types encompassing levels of biological organisation from molecule to organism [26]. …”

Section: Methodsmentioning

confidence: 99%

Wide coverage biomedical event extraction using multiple partially overlapping corpora

et al. 2013

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BackgroundBiomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes.ResultsWe propose a method for learning from multiple corpora with partial semantic annotation overlap, and implement this method to improve our existing event extraction system, EventMine. An evaluation using seven event annotated corpora, including 65 event types in total, shows that learning from overlapping corpora can produce a single, corpus-independent, wide coverage extraction system that outperforms systems trained on single corpora and exceeds previously reported results on two established event extraction tasks from the BioNLP Shared Task 2011.ConclusionsThe proposed method allows the training of a wide-coverage, state-of-the-art event extraction system from multiple corpora with partial semantic annotation overlap. The resulting single model makes broad-coverage extraction straightforward in practice by removing the need to either select a subset of compatible corpora or semantic types, or to merge results from several models trained on different individual corpora. Multi-corpus learning also allows annotation efforts to focus on covering additional semantic types, rather than aiming for exhaustive coverage in any single annotation effort, or extending the coverage of semantic types annotated in existing corpora.

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Section: Methodsmentioning

confidence: 99%

Wide coverage biomedical event extraction using multiple partially overlapping corpora

et al. 2013

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“…It can be observed that Method Precision Recall F1-Score SVM (Pyysalo et al, 2012) 81.44 69.48 75.67 SVM+W e (Zhou et al, 2014) 80.60 74.23 77.82 CNN (Wang et al, 2016a) 80 …”

Section: Category Wise Performance Analysismentioning

confidence: 99%

Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

Patchigolla¹,

Sahu²,

Anand³

2017

BioNLP 2017

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Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex handcrafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-ofart F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.

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“…This task helped shift the focus of relation extraction efforts from identifying simple binary interactions to identifying complex nested events that better represent the biological interactions stated frequently in text. Existing approaches to this task include SVM (Björne and Salakoski, 2013) other ML based approaches (Riedel and McCallum, 2011;Miwa et al, 2010Miwa et al, , 2012 In our work, we take inspiration from the Turk Event Extraction System (TEES) (Björne and Salakoski, 2013) (the event extraction system for EVEX) that has consistently been the top performer in these series of tasks. They represent events using a graph format and break the event extraction task into separate multi-class classification tasks using SVM as their classifier.…”

Section: Related Workmentioning

confidence: 99%

BioNLP 2017

Cohen¹,

Demner‐Fushman²,

Ananiadou³

et al. 2017

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According to the Association for Computational Linguistics guidelines on special interest groups (SIGs), The function of a SIG is to encourage interest and activity in specific areas within the ACL's field [1]. Is the SIGBioMed special interest group "within the ACL's field"? The titles of this year's papers suggest that it is, in that the current interest in deep learning in its many and varied manifestations is mirrored in those titles. Do those papers cover a specific area? They do, and in doing so, they demonstrate one of the great satisfactions of working in biomedical natural language processing.One of the joys of involvement in the biomedical natural language processing community is seeing the development of research with clinical applications. As examples of such work being presented at BioNLP 2017, we would like to point out the two papers that discuss the application of natural language processing to the diagnosis of neurological disorders. Bhatia et al. [2] describe an approach to using speech processing in the assessment of patients with amyotrophic lateral sclerosis (also known as Lou Gehrig's disease), one of the more horrific motor neuron diseases. Good assessment of amyotrophic lateral sclerosis patients is important for a number of reasons, including the fact that accurate tracking of the inevitable deterioration that is a hallmark of this disease gives patients and their families the possibility of purposeful planning for the attendant disability and death. However, current methodologies for evaluating the status of amyotrophic lateral sclerosis patients necessarily involve expensive equipment and highly trained personnel; when further developed, this methodology could make such evaluation much more, and more frequently, available to ALS patients. The fact that the work reported here involves a speech modality is especially exciting, as speech-related indicators of future ALS can be present long before diagnosis. The paper uses measurements of phonological features of speech and their divergence from a baseline, and demonstrates correlation with physiological measures.Adams et al. [3] describe work on detecting and categorizing word production errors associated with anomia, a particular kind of inability to find words. Screening for anomia is important because anomia is a symptom of stroke, but it is difficult and time-consuming to do, and therefore is not done as often as it should be. Automatic detection of anomia could be a nice enabler of improved care for stroke victims, but it is made difficult due to the subtlety of the phonological and semantic judgments that have to be made when assessing the phenomenon. The paper uses a combination of language modeling and phonologically-based edit distance calculation to approach the task, applying these techniques to data from the AphasiaBank collection of transcribed aphasic and healthy speech.Although we have summarized only these two examples that address neurological disorders, there are several other papers on the use of natural language proc...

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Event extraction across multiple levels of biological organization

Cited by 121 publications

References 32 publications

Wide coverage biomedical event extraction using multiple partially overlapping corpora

Wide coverage biomedical event extraction using multiple partially overlapping corpora

Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

BioNLP 2017

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