CHEMICAL NAMES STANDARDIZATION USING NEURAL SEQUENCE TO SEQUENCE MODEL
Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.
 This work presents a method to translate non-systematic names of chemical compounds into their systematic equivalents. Beyond that, a corpus of systematic and non-systematic chemical names is introduced that were extracted from chemistry and manually labelled. The paper is well-structured and the authors introduce the problem setting very nicely to a machine learning audience, explain the challenges and motivate the architecture of their model. The model is a combination of tested approaches such as a spelling error correction, a byte pair encoding tokenizer and a sequence-to-sequence model consisting of (Bi)LSTMs and attention mechanisms. The evaluation appears solid. The model achieves significantly improved results on the proposed corpus, even though compared to a underwhelming baseline. The analysis could be improved by showing and explaining some examples of failed translation attempts. It would also be interesting to see how the failed attempts are distributed over the error types (spelling, order, common name, synonym). The authors suggest a scenario where non-systematic names are converted and checked against a database of systematic names. For this, it would be interesting to know whether there are cases where a non-systematic name was translated into the wrong (but valid) systematic name. Concluding, the paper presents an interesting application for machine translation, a new dataset and a method that successfully labels 50% of the given corpus. Minor issue: The color scale of Fig. 5 is hard to recognize due to low contrast.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
CHEMICAL NAMES STANDARDIZATION USING NEURAL SEQUENCE TO SEQUENCE MODEL
Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization. Once a systematic name for a chemical compound is given, it will naturally and much simply convert the name into the eventually required molecular formula. However, for many chemical substances, they have been shown in many other names besides their systematic names which poses a great challenge for this task. In this paper, we propose a framework to do the auto standardization from the non-systematic names to the corresponding systematic names by using the spelling error correction, byte pair encoding tokenization and neural sequence to sequence model. Our framework is trained end to end and is fully data-driven. Our standardization accuracy on the test dataset achieves 54.04% which has a great improvement compared to previous state-of-the-art result.
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 work presents a method to translate non-systematic names of chemical compounds into their systematic equivalents. Beyond that, a corpus of systematic and non-systematic chemical names is introduced that were extracted from chemistry and manually labelled. The paper is well-structured and the authors introduce the problem setting very nicely to a machine learning audience, explain the challenges and motivate the architecture of their model. The model is a combination of tested approaches such as a spelling error correction, a byte pair encoding tokenizer and a sequence-to-sequence model consisting of (Bi)LSTMs and attention mechanisms. The evaluation appears solid. The model achieves significantly improved results on the proposed corpus, even though compared to a underwhelming baseline. The analysis could be improved by showing and explaining some examples of failed translation attempts. It would also be interesting to see how the failed attempts are distributed over the error types (spelling, order, common name, synonym). The authors suggest a scenario where non-systematic names are converted and checked against a database of systematic names. For this, it would be interesting to know whether there are cases where a non-systematic name was translated into the wrong (but valid) systematic name. Concluding, the paper presents an interesting application for machine translation, a new dataset and a method that successfully labels 50% of the given corpus. Minor issue: The color scale of Fig. 5 is hard to recognize due to low contrast.
assistant
 This work presents a method to translate non-systematic names of chemical compounds into their systematic equivalents. Beyond that, a corpus of systematic and non-systematic chemical names is introduced that were extracted from chemistry and manually labelled. The paper is well-structured and the authors introduce the problem setting very nicely to a machine learning audience, explain the challenges and motivate the architecture of their model. The model is a combination of tested approaches such as a spelling error correction, a byte pair encoding tokenizer and a sequence-to-sequence model consisting of (Bi)LSTMs and attention mechanisms. The evaluation appears solid. The model achieves significantly improved results on the proposed corpus, even though compared to a underwhelming baseline. The analysis could be improved by showing and explaining some examples of failed translation attempts. It would also be interesting to see how the failed attempts are distributed over the error types (spelling, order, common name, synonym). The authors suggest a scenario where non-systematic names are converted and checked against a database of systematic names. For this, it would be interesting to know whether there are cases where a non-systematic name was translated into the wrong (but valid) systematic name. Concluding, the paper presents an interesting application for machine translation, a new dataset and a method that successfully labels 50% of the given corpus. Minor issue: The color scale of Fig. 5 is hard to recognize due to low contrast.