基于自适应提升的嵌入式神经网络关系抽取
Ensemble Neural Relation Extraction with Adaptive Boosting
摘要：
关系抽取是从开放语料库中抽取新的关系事实的一种方法。以往的关系抽取方法都面临着标注错误和数据噪声的问题，这大大降低了模型的性能。在本文中，我们提出了一个集成神经网络模型-自适应Boosting LSTM与注意力，更有效地执行关系抽取。具体来说，我们的模型首先采用递归神经网络LSTM嵌入每个句子。然后，我们将注意力引入LSTM，考虑到句子中的单词对句子的语义意义的贡献并不相等。接下来，通过自适应提升，我们策略性地构建了几个这样的神经分类器。通过将多个这样的LSTM分类器与自适应提升进行集成，我们可以构建一个更有效和鲁棒的基于联合集成的神经网络的关系提取器。在真实数据集上的实验结果表明，该模型的性能优越，与现有模型相比，F1分数提高了约8%。
英文摘要：
Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equally to the semantic meaning of the sentence. Next via adaptive boosting, we build strategically several such neural classifiers. By ensembling multiple such LSTM classifiers with adaptive boosting, we could build a more effective and robust joint ensemble neural networks based relation extractor. Experiment results on real dataset demonstrate the superior performance of the proposed model, improving F1-score by about 8% compared to the state-of-the-art models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
