A novel text representation which enables image classifiers to perform text classification
We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems. We apply the technique to entity disambiguation of inventor names in US patents. The method involves converting text from each pairwise comparison between two inventor name records into a 2D RGB (stacked) image representation. We then train an image classification neural network to discriminate between such pairwise comparison images, and use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors), obtaining highly accurate results (F1: 99.09%, precision: 99.41%, recall: 98.76%). Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, such as disambiguation of academic publications, or for problems that require simultaneous classification of both text and images.
This paper proposes a method to map a pair of textual information into a 2D RGB image. They achieve this by heuristically creating a character map in a 2D matrix and then add color according to the character within the text (e.g. a line connecting bi-gram characters). To compare 2 textual pieces, different colors are used. This novel “text representation” can be fed to 2D convolutional neural networks (image classifiers). The proposed representation is evaluated on a patent inventor disambiguation task, and shown to slightly our perform existing methods. I would not recommend this paper because the method in this paper is not well motivated and the empirical results do not demonstrate the usefulness of the proposed method. In addition, the quality of writing can be improved in several places. The goal to capture string similarity (character overlaps) is well motivated for the name disambiguation task, but it is unfounded on why a 2D mapping would provide additional benefits from the existing features. The paper could be improved by discussing the strength and weaknesses between the existing features and the proposed representation. In the experiment, it is shown that a simple string similarity feature and a random forest (Kim et al., 2016) already achieve 99% in the F1 score. A small increment of 0.09 in the F1 score cannot justify the proposed algorithm. I think the author should select other tasks altogether. Finally, the fact that any random character map gives the same result is quite counter-intuitive and requires more analysis such as ablation on coloring (connecting lines, blue color, …). As for the writing, there are several issues. First, the format of the table is incorrect. Some tables are out of the border and the captions are on the top. More importantly, the method to construct the character map is unclear and a significant part of the map is left in the appendix.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A novel text representation which enables image classifiers to perform text classification
We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems. We apply the technique to entity disambiguation of inventor names in US patents. The method involves converting text from each pairwise comparison between two inventor name records into a 2D RGB (stacked) image representation. We then train an image classification neural network to discriminate between such pairwise comparison images, and use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors), obtaining highly accurate results (F1: 99.09%, precision: 99.41%, recall: 98.76%). Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, such as disambiguation of academic publications, or for problems that require simultaneous classification of both text and images.
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 proposes a method to map a pair of textual information into a 2D RGB image. They achieve this by heuristically creating a character map in a 2D matrix and then add color according to the character within the text (e.g. a line connecting bi-gram characters). To compare 2 textual pieces, different colors are used. This novel “text representation” can be fed to 2D convolutional neural networks (image classifiers). The proposed representation is evaluated on a patent inventor disambiguation task, and shown to slightly our perform existing methods. I would not recommend this paper because the method in this paper is not well motivated and the empirical results do not demonstrate the usefulness of the proposed method. In addition, the quality of writing can be improved in several places. The goal to capture string similarity (character overlaps) is well motivated for the name disambiguation task, but it is unfounded on why a 2D mapping would provide additional benefits from the existing features. The paper could be improved by discussing the strength and weaknesses between the existing features and the proposed representation. In the experiment, it is shown that a simple string similarity feature and a random forest (Kim et al., 2016) already achieve 99% in the F1 score. A small increment of 0.09 in the F1 score cannot justify the proposed algorithm. I think the author should select other tasks altogether. Finally, the fact that any random character map gives the same result is quite counter-intuitive and requires more analysis such as ablation on coloring (connecting lines, blue color, …). As for the writing, there are several issues. First, the format of the table is incorrect. Some tables are out of the border and the captions are on the top. More importantly, the method to construct the character map is unclear and a significant part of the map is left in the appendix.
assistant
This paper proposes a method to map a pair of textual information into a 2D RGB image. They achieve this by heuristically creating a character map in a 2D matrix and then add color according to the character within the text (e.g. a line connecting bi-gram characters). To compare 2 textual pieces, different colors are used. This novel “text representation” can be fed to 2D convolutional neural networks (image classifiers). The proposed representation is evaluated on a patent inventor disambiguation task, and shown to slightly our perform existing methods. I would not recommend this paper because the method in this paper is not well motivated and the empirical results do not demonstrate the usefulness of the proposed method. In addition, the quality of writing can be improved in several places. The goal to capture string similarity (character overlaps) is well motivated for the name disambiguation task, but it is unfounded on why a 2D mapping would provide additional benefits from the existing features. The paper could be improved by discussing the strength and weaknesses between the existing features and the proposed representation. In the experiment, it is shown that a simple string similarity feature and a random forest (Kim et al., 2016) already achieve 99% in the F1 score. A small increment of 0.09 in the F1 score cannot justify the proposed algorithm. I think the author should select other tasks altogether. Finally, the fact that any random character map gives the same result is quite counter-intuitive and requires more analysis such as ablation on coloring (connecting lines, blue color, …). As for the writing, there are several issues. First, the format of the table is incorrect. Some tables are out of the border and the captions are on the top. More importantly, the method to construct the character map is unclear and a significant part of the map is left in the appendix.