Classification Attention for Chinese NER
The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER). However, such model would likely miss the overall information of the entity words. In this paper, we propose to combine priori entity information with BERT. Instead of relying on additional lexicons or pre-trained word embeddings, our model has generated entity classification embeddings directly on the pre-trained BERT, having the merit of increasing model practicability and avoiding OOV problem. Experiments show that our model has achieved state-of-the-art results on 3 Chinese NER datasets.
This paper tries to improve the performance of Chinese NER by developing a novel attention mechanism that leverages BERT pre-trained model which considers bi-directional context. Experiments on a number of tasks show that the proposed approach is effective. Comments: [1] A bunch of experiments are conducted [2] Chinese NER is a hard problem, but it would be great to see the proposed approach generalizable to other tasks. So, the contribution of this paper is limited [3] The proposed algorithm is simple and effective, but the novelty is a bit low
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Classification Attention for Chinese NER
The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER). However, such model would likely miss the overall information of the entity words. In this paper, we propose to combine priori entity information with BERT. Instead of relying on additional lexicons or pre-trained word embeddings, our model has generated entity classification embeddings directly on the pre-trained BERT, having the merit of increasing model practicability and avoiding OOV problem. Experiments show that our model has achieved state-of-the-art results on 3 Chinese NER datasets.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper tries to improve the performance of Chinese NER by developing a novel attention mechanism that leverages BERT pre-trained model which considers bi-directional context. Experiments on a number of tasks show that the proposed approach is effective. Comments: [1] A bunch of experiments are conducted [2] Chinese NER is a hard problem, but it would be great to see the proposed approach generalizable to other tasks. So, the contribution of this paper is limited [3] The proposed algorithm is simple and effective, but the novelty is a bit low
assistant
This paper tries to improve the performance of Chinese NER by developing a novel attention mechanism that leverages BERT pre-trained model which considers bi-directional context. Experiments on a number of tasks show that the proposed approach is effective. Comments: [1] A bunch of experiments are conducted [2] Chinese NER is a hard problem, but it would be great to see the proposed approach generalizable to other tasks. So, the contribution of this paper is limited [3] The proposed algorithm is simple and effective, but the novelty is a bit low