Investigating Different Context Types and Representations for Learning Word Embeddings
The number of word embedding models is growing every year. Most of them learn word embeddings based on the co-occurrence information of words and their context. However, it*s still an open question what is the best definition of context. We provide the first systematical investigation of different context types and representations for learning word embeddings. We conduct comprehensive experiments to evaluate their effectiveness under 4 tasks (21 datasets), which give us some insights about context selection. We hope that this paper, along with the published code, can serve as a guideline of choosing context for our community.
This paper analyzes dependency trees vs standard window contexts for word vector learning. While that*s a good goal I believe the paper falls short of a thorough analysis of the subject matter. It does not analyze Glove like objective functions which often work better than the algorithms used here. It doesn*t compare in absolute terms to other published vectors or models. It fails to gain any particularly interesting insights that will modify other people*s work. It fails to push the state of the art or make available new resources for people.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Investigating Different Context Types and Representations for Learning Word Embeddings
The number of word embedding models is growing every year. Most of them learn word embeddings based on the co-occurrence information of words and their context. However, it*s still an open question what is the best definition of context. We provide the first systematical investigation of different context types and representations for learning word embeddings. We conduct comprehensive experiments to evaluate their effectiveness under 4 tasks (21 datasets), which give us some insights about context selection. We hope that this paper, along with the published code, can serve as a guideline of choosing context for our community.
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 analyzes dependency trees vs standard window contexts for word vector learning. While that*s a good goal I believe the paper falls short of a thorough analysis of the subject matter. It does not analyze Glove like objective functions which often work better than the algorithms used here. It doesn*t compare in absolute terms to other published vectors or models. It fails to gain any particularly interesting insights that will modify other people*s work. It fails to push the state of the art or make available new resources for people.
assistant
This paper analyzes dependency trees vs standard window contexts for word vector learning. While that*s a good goal I believe the paper falls short of a thorough analysis of the subject matter. It does not analyze Glove like objective functions which often work better than the algorithms used here. It doesn*t compare in absolute terms to other published vectors or models. It fails to gain any particularly interesting insights that will modify other people*s work. It fails to push the state of the art or make available new resources for people.