Mapping the hyponymy relation of wordnet onto vector Spaces
In this paper, we investigate mapping the hyponymy relation of wordnet to feature vectors. We aim to model lexical knowledge in such a way that it can be used as input in generic machine-learning models, such as phrase entailment predictors. We propose two models. The first one leverages an existing mapping of words to feature vectors (fasttext), and attempts to classify such vectors as within or outside of each class. The second model is fully supervised, using solely wordnet as a ground truth. It maps each concept to an interval or a disjunction thereof. On the first model, we approach, but not quite attain state of the art performance. The second model can achieve near-perfect accuracy.
 This paper explores the notion of hyponymy in word vector representations. It tests the capacity of a logistic regression classifier to distinguish words that are and are not hyponyms using fastText embeddings, and it also describes a method of organizing WordNet relations into a tree structure and defining hyponymy based on this structure. The problem of capturing hyponymy relations within vector space models of word representation is an interesting and important one, but it is not clear to me that this paper has made a substantive contribution to it. The paper seems simply to 1) observe that fastText embeddings are imperfect for hyponymy detection with a linear classifier, and 2) reconstruct the fairly natural interpretation of WordNet relations as a hierarchical tree structure, and to re-extract hyponymy relations from that tree structure. As far as I can tell, the paper*s “supervised” model does not use embeddings (or learning) at all. Assessing the paper*s contribution is made more difficult by an overall lack of clarity. The details of the experiments are not laid out with sufficient explicitness, and the reporting of results is also fairly confusing (I am not clear, for example, on what is depicted in Figure 2). The paper is not organized in a particularly intuitive way, nor has it made clear what the contributions might be. Overall, while I think that this is a worthy topic, I do not think that the contribution or the clarity of this paper are currently sufficient for publication. Additional comments: -The PCA plot is too dense to be a useful visual - it would be more useful to plot a smaller number of relevant points. -Results should be presented more clearly in table form - there seem to be a large number of results that are not reported in any table (for instance, the results described in Section 3).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Mapping the hyponymy relation of wordnet onto vector Spaces
In this paper, we investigate mapping the hyponymy relation of wordnet to feature vectors. We aim to model lexical knowledge in such a way that it can be used as input in generic machine-learning models, such as phrase entailment predictors. We propose two models. The first one leverages an existing mapping of words to feature vectors (fasttext), and attempts to classify such vectors as within or outside of each class. The second model is fully supervised, using solely wordnet as a ground truth. It maps each concept to an interval or a disjunction thereof. On the first model, we approach, but not quite attain state of the art performance. The second model can achieve near-perfect accuracy.
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 explores the notion of hyponymy in word vector representations. It tests the capacity of a logistic regression classifier to distinguish words that are and are not hyponyms using fastText embeddings, and it also describes a method of organizing WordNet relations into a tree structure and defining hyponymy based on this structure. The problem of capturing hyponymy relations within vector space models of word representation is an interesting and important one, but it is not clear to me that this paper has made a substantive contribution to it. The paper seems simply to 1) observe that fastText embeddings are imperfect for hyponymy detection with a linear classifier, and 2) reconstruct the fairly natural interpretation of WordNet relations as a hierarchical tree structure, and to re-extract hyponymy relations from that tree structure. As far as I can tell, the paper*s “supervised” model does not use embeddings (or learning) at all. Assessing the paper*s contribution is made more difficult by an overall lack of clarity. The details of the experiments are not laid out with sufficient explicitness, and the reporting of results is also fairly confusing (I am not clear, for example, on what is depicted in Figure 2). The paper is not organized in a particularly intuitive way, nor has it made clear what the contributions might be. Overall, while I think that this is a worthy topic, I do not think that the contribution or the clarity of this paper are currently sufficient for publication. Additional comments: -The PCA plot is too dense to be a useful visual - it would be more useful to plot a smaller number of relevant points. -Results should be presented more clearly in table form - there seem to be a large number of results that are not reported in any table (for instance, the results described in Section 3).
assistant
 This paper explores the notion of hyponymy in word vector representations. It tests the capacity of a logistic regression classifier to distinguish words that are and are not hyponyms using fastText embeddings, and it also describes a method of organizing WordNet relations into a tree structure and defining hyponymy based on this structure. The problem of capturing hyponymy relations within vector space models of word representation is an interesting and important one, but it is not clear to me that this paper has made a substantive contribution to it. The paper seems simply to 1) observe that fastText embeddings are imperfect for hyponymy detection with a linear classifier, and 2) reconstruct the fairly natural interpretation of WordNet relations as a hierarchical tree structure, and to re-extract hyponymy relations from that tree structure. As far as I can tell, the paper*s “supervised” model does not use embeddings (or learning) at all. Assessing the paper*s contribution is made more difficult by an overall lack of clarity. The details of the experiments are not laid out with sufficient explicitness, and the reporting of results is also fairly confusing (I am not clear, for example, on what is depicted in Figure 2). The paper is not organized in a particularly intuitive way, nor has it made clear what the contributions might be. Overall, while I think that this is a worthy topic, I do not think that the contribution or the clarity of this paper are currently sufficient for publication. Additional comments: -The PCA plot is too dense to be a useful visual - it would be more useful to plot a smaller number of relevant points. -Results should be presented more clearly in table form - there seem to be a large number of results that are not reported in any table (for instance, the results described in Section 3).