Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of *outlier* elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task. We used our methodology to create a gold-standard dataset, which we call WikiSem500, and evaluated multiple state-of-the-art embeddings. The results show a correlation between performance on this dataset and performance on sentiment analysis.
This paper introduces a new dataset to evaluate word representations. The task considered in the paper, called outlier detection (also known as word intrusion), is to identify which word does not belong to a set of semantically related words. The task was proposed by Camacho-Collados & Navigli (2016) as an evaluation of word representations. The main contribution of this paper is to introduce a new dataset for this task, covering 5 languages. The dataset was generated automatically from the Wikidata hierarchy. Entities which are instances of the same category are considered as belonging to the same cluster, and outliers are sampled at various distances in the tree. Several heuristics are then proposed to exclude uninteresting clusters from the dataset. Developing good ressources to evaluate word representations is an important task. The new dataset introduced in this paper might be an interesting addition to the existing ones (however, it is hard to say by only reviewing the paper). I am a bit concerned by the lack of discussion and comparison with existing approaches (besides word similarity datasets). In particular, I believe it would be interesting to discuss the advantages of this evaluation/dataset, compared to existing ones such as word analogies. The proposed evaluation also seems highly related to entity typing, which is not discussed in the paper. Overall, I believe that introducing ressources for evaluating word representations is very important for the community. However, I am a bit ambivalent about this submission. I am not entirely convinced that the proposed dataset have clear advantages over existing ressources. It also seems that existing tasks, such as entity typing, already capture similar properties of word representations. Finally, it might be more relevant to submit this paper to LREC than to ICLR.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations
We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of *outlier* elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task. We used our methodology to create a gold-standard dataset, which we call WikiSem500, and evaluated multiple state-of-the-art embeddings. The results show a correlation between performance on this dataset and performance on sentiment analysis.
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 introduces a new dataset to evaluate word representations. The task considered in the paper, called outlier detection (also known as word intrusion), is to identify which word does not belong to a set of semantically related words. The task was proposed by Camacho-Collados & Navigli (2016) as an evaluation of word representations. The main contribution of this paper is to introduce a new dataset for this task, covering 5 languages. The dataset was generated automatically from the Wikidata hierarchy. Entities which are instances of the same category are considered as belonging to the same cluster, and outliers are sampled at various distances in the tree. Several heuristics are then proposed to exclude uninteresting clusters from the dataset. Developing good ressources to evaluate word representations is an important task. The new dataset introduced in this paper might be an interesting addition to the existing ones (however, it is hard to say by only reviewing the paper). I am a bit concerned by the lack of discussion and comparison with existing approaches (besides word similarity datasets). In particular, I believe it would be interesting to discuss the advantages of this evaluation/dataset, compared to existing ones such as word analogies. The proposed evaluation also seems highly related to entity typing, which is not discussed in the paper. Overall, I believe that introducing ressources for evaluating word representations is very important for the community. However, I am a bit ambivalent about this submission. I am not entirely convinced that the proposed dataset have clear advantages over existing ressources. It also seems that existing tasks, such as entity typing, already capture similar properties of word representations. Finally, it might be more relevant to submit this paper to LREC than to ICLR.
assistant
This paper introduces a new dataset to evaluate word representations. The task considered in the paper, called outlier detection (also known as word intrusion), is to identify which word does not belong to a set of semantically related words. The task was proposed by Camacho-Collados & Navigli (2016) as an evaluation of word representations. The main contribution of this paper is to introduce a new dataset for this task, covering 5 languages. The dataset was generated automatically from the Wikidata hierarchy. Entities which are instances of the same category are considered as belonging to the same cluster, and outliers are sampled at various distances in the tree. Several heuristics are then proposed to exclude uninteresting clusters from the dataset. Developing good ressources to evaluate word representations is an important task. The new dataset introduced in this paper might be an interesting addition to the existing ones (however, it is hard to say by only reviewing the paper). I am a bit concerned by the lack of discussion and comparison with existing approaches (besides word similarity datasets). In particular, I believe it would be interesting to discuss the advantages of this evaluation/dataset, compared to existing ones such as word analogies. The proposed evaluation also seems highly related to entity typing, which is not discussed in the paper. Overall, I believe that introducing ressources for evaluating word representations is very important for the community. However, I am a bit ambivalent about this submission. I am not entirely convinced that the proposed dataset have clear advantages over existing ressources. It also seems that existing tasks, such as entity typing, already capture similar properties of word representations. Finally, it might be more relevant to submit this paper to LREC than to ICLR.