Event extraction from unstructured Amharic text
In information extraction, event extraction is one of the types that extract the specific knowledge of certain incidents from texts. Event extraction has been done on different languages texts but not on one of the Semitic language Amharic. In this study, we present a system that extracts an event from unstructured Amharic text. The system has designed by the integration of supervised machine learning and rule-based approaches together. We call it a hybrid system. The model from the supervised machine learning detects events from the text, then, handcrafted rules and the rule-based rules extract the event from the text. The hybrid system has compared with the standalone rule-based method that is well known for event extraction. The study has shown that the hybrid system has outperformed the standalone rule-based method. For the event extraction, we have been extracting event arguments. Event arguments identify event triggering words or phrases that clearly express the occurrence of the event. The event argument attributes can be verbs, nouns, occasionally adjectives such as ???/wedding and time as well.
This paper deals with the important issue of information extraction from less-resourced languages. In its current form, the paper has two main weaknesses: - it is poorly written & organized - it was a fairly weak empirical evaluation In order to address the first issue: - the authors should significantly improve the quality of the prose, which can be confusing & difficult to undersrand - the introduction needs to be significantly crisper: in its current form, it is far too general and does NOT describe the rest of the paper; the authors should explain ... 1) what is the problem they are working on (currently present, but far too long) 2) what is the proposed approach & why is it novel (missing) 3) what are the main results & their significance (also missing) In order to address the second issue: - 3.1 needs more details; it is this reviewer*s understanding that the current corpus consists of 1065 documents (which is extremely small in size); how many sentences are there in these documents? how many on/off events? - 3.2 is particularly difficult to follow - 3.3 should not use 3-4 paragraphs on introducing NB, SVM, and Decision Trees; last but not least, why decision trees and not randomized forests or deep learning models? in the current form, it is also unclear whether the on/off event detection is performed at sentence or document level - 4: what is the value of k in k-fold CV? Last but not least, the authors could use the work below as inspiration on how to improve the overall quality of the paper https://pqdtopen.proquest.com/doc/2025917601.html?FMT=ABS
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Event extraction from unstructured Amharic text
In information extraction, event extraction is one of the types that extract the specific knowledge of certain incidents from texts. Event extraction has been done on different languages texts but not on one of the Semitic language Amharic. In this study, we present a system that extracts an event from unstructured Amharic text. The system has designed by the integration of supervised machine learning and rule-based approaches together. We call it a hybrid system. The model from the supervised machine learning detects events from the text, then, handcrafted rules and the rule-based rules extract the event from the text. The hybrid system has compared with the standalone rule-based method that is well known for event extraction. The study has shown that the hybrid system has outperformed the standalone rule-based method. For the event extraction, we have been extracting event arguments. Event arguments identify event triggering words or phrases that clearly express the occurrence of the event. The event argument attributes can be verbs, nouns, occasionally adjectives such as ???/wedding and time as well.
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 deals with the important issue of information extraction from less-resourced languages. In its current form, the paper has two main weaknesses: - it is poorly written & organized - it was a fairly weak empirical evaluation In order to address the first issue: - the authors should significantly improve the quality of the prose, which can be confusing & difficult to undersrand - the introduction needs to be significantly crisper: in its current form, it is far too general and does NOT describe the rest of the paper; the authors should explain ... 1) what is the problem they are working on (currently present, but far too long) 2) what is the proposed approach & why is it novel (missing) 3) what are the main results & their significance (also missing) In order to address the second issue: - 3.1 needs more details; it is this reviewer*s understanding that the current corpus consists of 1065 documents (which is extremely small in size); how many sentences are there in these documents? how many on/off events? - 3.2 is particularly difficult to follow - 3.3 should not use 3-4 paragraphs on introducing NB, SVM, and Decision Trees; last but not least, why decision trees and not randomized forests or deep learning models? in the current form, it is also unclear whether the on/off event detection is performed at sentence or document level - 4: what is the value of k in k-fold CV? Last but not least, the authors could use the work below as inspiration on how to improve the overall quality of the paper https://pqdtopen.proquest.com/doc/2025917601.html?FMT=ABS
assistant
This paper deals with the important issue of information extraction from less-resourced languages. In its current form, the paper has two main weaknesses: - it is poorly written & organized - it was a fairly weak empirical evaluation In order to address the first issue: - the authors should significantly improve the quality of the prose, which can be confusing & difficult to undersrand - the introduction needs to be significantly crisper: in its current form, it is far too general and does NOT describe the rest of the paper; the authors should explain ... 1) what is the problem they are working on (currently present, but far too long) 2) what is the proposed approach & why is it novel (missing) 3) what are the main results & their significance (also missing) In order to address the second issue: - 3.1 needs more details; it is this reviewer*s understanding that the current corpus consists of 1065 documents (which is extremely small in size); how many sentences are there in these documents? how many on/off events? - 3.2 is particularly difficult to follow - 3.3 should not use 3-4 paragraphs on introducing NB, SVM, and Decision Trees; last but not least, why decision trees and not randomized forests or deep learning models? in the current form, it is also unclear whether the on/off event detection is performed at sentence or document level - 4: what is the value of k in k-fold CV? Last but not least, the authors could use the work below as inspiration on how to improve the overall quality of the paper https://pqdtopen.proquest.com/doc/2025917601.html?FMT=ABS