1,255 research outputs found
Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models
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 hand-crafted 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-of-art 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.Comment: The work has been accepted in BioNLP at ACL-201
Discovering Power Laws in Entity Length
This paper presents a discovery that the length of the entities in various
datasets follows a family of scale-free power law distributions. The concept of
entity here broadly includes the named entity, entity mention, time expression,
aspect term, and domain-specific entity that are well investigated in natural
language processing and related areas. The entity length denotes the number of
words in an entity. The power law distributions in entity length possess the
scale-free property and have well-defined means and finite variances. We
explain the phenomenon of power laws in entity length by the principle of least
effort in communication and the preferential mechanism
External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway
This paper evaluates the impact of various event extraction systems on
automatic pathway curation using the popular mTOR pathway. We quantify the
impact of training data sets as well as different machine learning classifiers
and show that some improve the quality of automatically extracted pathways
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