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Hokkaido University Collection of Scholarly and Academic Papers >
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Data filtering in humor generation: comparative analysis of hit rate and co-occurrence rankings as a method to choose usable pun candidates
| Title: | Data filtering in humor generation: comparative analysis of hit rate and co-occurrence rankings as a method to choose usable pun candidates |
| Authors: | Dybala, Pawel Browse this author | | Rzepka, Rafal Browse this author →KAKEN DB | | Araki, Kenji Browse this author →KAKEN DB | | Sayama, Kohichi Browse this author →KAKEN DB |
| Keywords: | humor processing | | web-based data extraction | | AI | | NLP | | HCI |
| Issue Date: | 29-Oct-2012 |
| Publisher: | ACM |
| Journal Title: | CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management |
| Start Page: | 2587 |
| End Page: | 2590 |
| Publisher DOI: | 10.1145/2396761.2398698 |
| Abstract: | In this paper we propose a method of filtering excessive amount of textual data acquired from the Internet. In our research on pun generation in Japanese we experienced problems with extensively long data processing time, caused by the amount of phonetic candidates generated (i.e. phrases that can be used to generate actual puns) by our system. Simple, naive approach in which we take into considerations only phrases with the highest occurrence in the Internet, can effect in deletion of those candidates that are actually usable. Thus, we propose a data filtering method in which we compare two Internet-based rankings: a co-occurrence ranking and a hit rate ranking, and select only candidates which occupy the same or similar positions in these rankings. In this work we analyze the effects of such data reduction, considering 1 cases: when the candidates are on exactly the same positions in both rankings, and when their positions differ by 1, 2, 3 and 4. The analysis is conducted on data acquired by comparing pun candidates generated by the system (and filtered with our method) with phrases that were actually used in puns created by humans. The results show that the proposed method can be used to filter excessive amounts of textual data acquired from the Internet. |
| Conference Name: | ACM international conference on Information and knowledge management |
| Conference Sequence: | 21 |
| Conference Place: | Maui, Hawaii |
| Rights: | © 2012 ACM. This is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management , Pages 2587-2590 ,2012-10-29,http://doi.acm.org/10.1145/2396761.2398698 |
| Type: | proceedings (author version) |
| URI: | http://hdl.handle.net/2115/63987 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: RZEPKA Rafal
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