Abstract
Wide attention has been paid to named entity recognition (NER) in specific fields. Among the representative tasks are the aspect term extraction (ATE) in user online comments and the biomedical named entity recognition (BioNER) in medical documents. Existing methods only perform well in a particular field, and it is difficult to maintain an advantage in other fields. In this article, we propose a supervised learning method that can be used for much special domain NER tasks. The model consists of two parts, a multidimensional self-attention (MDSA) network and a CNN-based model. The multidimensional self-attention mechanism can calculate the importance of the context to the current word, select the relevance according to the importance, and complete the update of the word vector. This update mechanism allows the subsequent CNN model to have variable-length memory of sentence context. We conduct experiments on benchmark datasets of ATE and BioNER tasks. The results show that our model surpasses most baseline methods.
Highlights
With the rapid growth of Internet data, people urgently need to obtain valuable information from massive unstructured text
Most of the research studies are based on the extraction of place names or organization names from general datasets and pay little attention to specific fields. is paper explores aspect term extraction in user online comments and the biomedical named entity recognition in medical documents
For the Aspect term extraction (ATE) task, with extensive experiments on the SemEval-2014 dataset and the SemEval2016 dataset, the results indicate that our multidimensional self-attention (MDSA)-convolutional neural network (CNN) model is superior to other baseline methods in aspect term extraction for aspect-level sentiment analysis tasks
Summary
With the rapid growth of Internet data, people urgently need to obtain valuable information from massive unstructured text. Many researchers used the convolutional neural network (CNN) and long-short term memory network (LSTM) to obtain good results in a particular specialty field Their mining of the relationship between entities and context is insufficient. We propose a multidimensional self-attention CNN (MDSA-CNN) model for the named entity recognition task in a special field. For the ATE task, with extensive experiments on the SemEval-2014 dataset and the SemEval2016 dataset, the results indicate that our MDSA-CNN model is superior to other baseline methods in aspect term extraction for aspect-level sentiment analysis tasks. Winners [5, 6, 11] of SemEval aspect-based sentiment analysis (ABSA) challenges employed traditional sequence models, such as CRFs and maximum entropy (ME), to extract target words. We use the word vector processed by the attention mechanism as the network input of the four-layer CNN. E results of the CNN layer will be processed by the output layer. e calculated results represent the label distribution for each location. e output layer contains the fully connected layer, the softmax function, and the dropout method
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