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˜rg/wedding and time as well.
The paper studies extracting events from unstructured text, specifically on the low resource language Amharic. The paper proposes to combine rule-based based with a learning-based approach. 
Strength
-	Looks at the low resource language Amharic and mines a large corpus of text.
-	The paper compares 3 learning algorithms, NB, Libsvm, J48. As well as a hybrid versus a rule based approach.
Weaknesses:
1.	Prior work
1.1.	The paper misses to discuss prior work on Amahric copora
Rychlý P., Suchomel V. (2016) Annotated Amharic Corpora. In: Sojka P., Horák A., Kopeček I., Pala K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science, vol 9924. Springer, Cham
2.	Experimental evaluation
2.1.	Missing ablations
2.1.1.	The paper claims “Amharic presents sophisticated language-specific issues” but does not evaluate how the proposed approach handles those specific challenges.
2.1.2.	The authors “normalize” characters with the same meaning “to an ordinary conceivable form” which “helps the performance of [their] system”; however, the paper does not ablate this benefit.
2.2.	The authors “applied k-fold cross- validation which split our dataset randomly into k groups”. It would be great if the authors report the standard deviation across the results to understand the significant of differences.
2.3.	The paper misses to show any qualitative results showing how the system works and which error it makes. 
2.4.	The paper notes that the “Amharic has a subject-object verb agreement and other morphological feature makes cumbersome the construction of rule” as well as the large unsupervised corpus they have available suggest to me that the authors should explore unsupervised pre-training a transformer-based network, such as BERT or other approaches which have successfully been used on other languages.
3.	Clarity: The paper’s clarity could be improved:
3.1.	On the one hand by clearly stating the contributions and by improving the clarity of the writing throughout.
3.2.	It is not clear how Table 2 relates Table 1. The authors note it is the same dataset. Does Table 2 show Weighted Ave of On and Off-event?
3.3.	What is the large unlabeled corpus used for?
Minor:
- considering using the correct citation style: “27 million speakers Mulugeta and Gasser (2012) “ -> 27 million speakers (Mulugeta and Gasser, 2012)
- don’t capitilzie after “;”: 
- duplication of very similar sentences: “Events in Amharic text are predominantly expressed through verbs and nouns, but, the linguistic structure and morphological richness has not represented in models that have been developed for other languages. Events in Amharic are ex- pressed predominantly through verbs and nominal, but, the linguistic structure and morphological richness highly matter to apply models used for other languages.”
Conclusion:
While it is interesting and valuable that this paper studies a low-resource language. The paper misses comparisons to prior work for event extraction and misses to include ablations which show the value of the introduced rules and design decisions made. 
=== post author response ====
I thank the authors for their response. 
Unfortunately, the authors did not use the chance to update the pdf to improve the clarity (3.); report standard deviation (2.2) to judge significance, although they should have this data; nor did they include a revised discussion of prior work.
As clarity and experimental evaluation are also major concerns of the other reviewers and it is unclear if and how they will be addressed in a revision I do not recommend accepting the paper.