Adaptable, high recall, event extraction system with minimal configuration.
Adaptable, high recall, event extraction system with minimal configuration.
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DOI：
10.1186/1471-2105-16-s10-s7
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发表时间：
2015
影响因子：
3
通讯作者：
Ananiadou S
中科院分区：
文献类型：
--
作者：
Miwa M;Ananiadou S
自2009年第一个生物医学自然语言处理（BioNLP）共享任务以来，生物医学事件提取一直是生物医学自然语言处理（BioNLP）研究的主要焦点。因此，已经开发了大量的事件提取系统。然而，大多数这样的系统已经被开发用于特定的任务和/或合并的任务特定的设置，使得它们在不修改系统本身的情况下应用于新的语料库和任务是有问题的。因此，需要这样的事件提取系统，其在应用于新领域中的语料库时可以实现高水平的准确性，而不需要详尽的调整或修改，同时保持有竞争力的性能水平。我们增强了最先进的事件提取系统EventMine，以减轻对特定任务调优的需求。特定于任务的详细信息在配置文件中指定，同时通过集成加权方法、协变量移位方法及其组合来避免大量特定于任务的参数调优。在BioNLP共享任务2013的两个不同子任务（即癌症遗传学（CG）和路径策展（PC））的背景下，采用了特定于任务的配置和加权方法，消除了针对每个任务专门修改系统的需要。通过最少的任务特定配置和调整，EventMine在PC任务中获得了第一名，在CG中获得了第二名，在这两项任务中都获得了最高的召回率。该系统已被进一步增强后，共享的任务，结合协变量转移方法和实体概括的任务定义的基础上，导致进一步的性能改进。我们已经证明，它是可能的，适用于一个国家的最先进的事件提取系统的新任务，具有高水平的性能，而不必修改系统内部。协变量移位和加权方法在促进高召回系统的产生方面是有用的。这些方法及其组合可以使模型适应目标数据，而无需深度调优和少量手动配置。
Biomedical event extraction has been a major focus of biomedical natural language processing (BioNLP) research since the first BioNLP shared task was held in 2009. Accordingly, a large number of event extraction systems have been developed. Most such systems, however, have been developed for specific tasks and/or incorporated task specific settings, making their application to new corpora and tasks problematic without modification of the systems themselves. There is thus a need for event extraction systems that can achieve high levels of accuracy when applied to corpora in new domains, without the need for exhaustive tuning or modification, whilst retaining competitive levels of performance. We have enhanced our state-of-the-art event extraction system, EventMine, to alleviate the need for task-specific tuning. Task-specific details are specified in a configuration file, while extensive task-specific parameter tuning is avoided through the integration of a weighting method, a covariate shift method, and their combination. The task-specific configuration and weighting method have been employed within the context of two different sub-tasks of BioNLP shared task 2013, i.e. Cancer Genetics (CG) and Pathway Curation (PC), removing the need to modify the system specifically for each task. With minimal task specific configuration and tuning, EventMine achieved the 1st place in the PC task, and 2nd in the CG, achieving the highest recall for both tasks. The system has been further enhanced following the shared task by incorporating the covariate shift method and entity generalisations based on the task definitions, leading to further performance improvements. We have shown that it is possible to apply a state-of-the-art event extraction system to new tasks with high levels of performance, without having to modify the system internally. Both covariate shift and weighting methods are useful in facilitating the production of high recall systems. These methods and their combination can adapt a model to the target data with no deep tuning and little manual configuration.