IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source

Sofia Reznikova, Leon Derczynski


Abstract
This paper describes the IUCM entry at SemEval-2018 Task 11, on machine comprehension using commonsense knowledge. First, clustering and topic modeling are used to divide given texts into topics. Then, during the answering phase, other texts of the same topic are retrieved and used as commonsense knowledge. Finally, the answer is selected. While clustering itself shows good results, finding an answer proves to be more challenging. This paper reports the results of system evaluation and suggests potential improvements.
Anthology ID:
S18-1179
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1068–1072
Language:
URL:
https://aclanthology.org/S18-1179
DOI:
10.18653/v1/S18-1179
Bibkey:
Cite (ACL):
Sofia Reznikova and Leon Derczynski. 2018. IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1068–1072, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source (Reznikova & Derczynski, SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1179.pdf
Code
 sonyareznikova/semeval2018task11