Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction
We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces (N-balls for short). Inclusion relations among N-balls implicitly encode subordinate relations among categories. The similarity measurement in terms of the cosine function is enriched by category information. Using a geometric construction method instead of back-propagation, we create large N-ball embeddings that satisfy two conditions: (1) category trees are precisely imposed onto word embeddings at zero energy cost; (2) pre-trained word embeddings are well preserved. A new benchmark data set is created for validating the category of unknown words. Experiments show that N-ball embeddings, carrying category information, significantly outperform word embeddings in the test of nearest neighborhoods, and demonstrate surprisingly good performance in validating categories of unknown words. Source codes and data-sets are free for public access url{https://github.com/gnodisnait/nball4tree.git} and url{https://github.com/gnodisnait/bp94nball.git}.
 This paper focuses on adjusting the pretrained word embeddings so that they respect the hypernymy/hyponymy relationship by appropriate n-ball encapsulation. They propose to do so by augmenting the word embeddings with information from a resource like Wordnet and applying 3 kinds of geometric transformations to enforce the encapsulation. The motivation of doing this is not very clear and experimental results are mainly qualitative (and subjective) showing that hypernymy relation can be predicted and preserved by their adjustment. Since, this work relies on Wordnet, the coverage of vocabulary is severely limited and as the authors discus in the results with the section titled "Experiment 3: Method 2", they had to remove many words in the standard semantic similarity datasets which casts shadow on the usefulness of the proposed approach. It is unclear what the main contribution of such an approach. Apart from this, the paper is diffcult to read and some parts (especially those pertaining to Figure 3) encode a simple concept that has been expressed in a very complicated manner. Overall, I give a score of 4 because of the limited coverage of the approach because of reliance on Wordnet and inadequate empirical evidence of usefulness of this approach.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction
We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces (N-balls for short). Inclusion relations among N-balls implicitly encode subordinate relations among categories. The similarity measurement in terms of the cosine function is enriched by category information. Using a geometric construction method instead of back-propagation, we create large N-ball embeddings that satisfy two conditions: (1) category trees are precisely imposed onto word embeddings at zero energy cost; (2) pre-trained word embeddings are well preserved. A new benchmark data set is created for validating the category of unknown words. Experiments show that N-ball embeddings, carrying category information, significantly outperform word embeddings in the test of nearest neighborhoods, and demonstrate surprisingly good performance in validating categories of unknown words. Source codes and data-sets are free for public access url{https://github.com/gnodisnait/nball4tree.git} and url{https://github.com/gnodisnait/bp94nball.git}.
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 focuses on adjusting the pretrained word embeddings so that they respect the hypernymy/hyponymy relationship by appropriate n-ball encapsulation. They propose to do so by augmenting the word embeddings with information from a resource like Wordnet and applying 3 kinds of geometric transformations to enforce the encapsulation. The motivation of doing this is not very clear and experimental results are mainly qualitative (and subjective) showing that hypernymy relation can be predicted and preserved by their adjustment. Since, this work relies on Wordnet, the coverage of vocabulary is severely limited and as the authors discus in the results with the section titled "Experiment 3: Method 2", they had to remove many words in the standard semantic similarity datasets which casts shadow on the usefulness of the proposed approach. It is unclear what the main contribution of such an approach. Apart from this, the paper is diffcult to read and some parts (especially those pertaining to Figure 3) encode a simple concept that has been expressed in a very complicated manner. Overall, I give a score of 4 because of the limited coverage of the approach because of reliance on Wordnet and inadequate empirical evidence of usefulness of this approach.
assistant
 This paper focuses on adjusting the pretrained word embeddings so that they respect the hypernymy/hyponymy relationship by appropriate n-ball encapsulation. They propose to do so by augmenting the word embeddings with information from a resource like Wordnet and applying 3 kinds of geometric transformations to enforce the encapsulation. The motivation of doing this is not very clear and experimental results are mainly qualitative (and subjective) showing that hypernymy relation can be predicted and preserved by their adjustment. Since, this work relies on Wordnet, the coverage of vocabulary is severely limited and as the authors discus in the results with the section titled "Experiment 3: Method 2", they had to remove many words in the standard semantic similarity datasets which casts shadow on the usefulness of the proposed approach. It is unclear what the main contribution of such an approach. Apart from this, the paper is diffcult to read and some parts (especially those pertaining to Figure 3) encode a simple concept that has been expressed in a very complicated manner. Overall, I give a score of 4 because of the limited coverage of the approach because of reliance on Wordnet and inadequate empirical evidence of usefulness of this approach.