@inproceedings{68b3ef4ac05b4ce48cc878ae5ffd050f,
title = "Claim Retrieval in Twitter",
abstract = "Controversial topics, especially the new emerging ones are widely discussed and searched in social medias like Twitter. When people are interested in topics and search on Twitter, high quality tweets are expected to appear at the top. Since it is only argumentation that truly reasons things out, we believe that high quality tweets are those with argumentation that consists of claim and evidence. Moreover, claim is the heart of argumentation, we concentrate on claim retrieval in Twitter. Based on a learning-to-rank framework, we integrate Twitter structural information and topic-independent claim-related lexicon to re-rank the relevant tweet list pre-retrieved by BM25 scores. We also automatically construct topic-dependent claim-oriented lexicons to further elevate the retrieval performance. Additionally, our model can be easily adapted to new topics without any manual process or external information, which guarantees the practicability of our model.",
keywords = "Claim retrieval, Claim-oriented lexicon, Topic adaptable, Twitter structural information",
author = "Wenjia Ma and Wenhan Chao and Zhunchen Luo and Xin Jiang",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 19th International Conference on Web Information Systems Engineering, WISE 2018 ; Conference date: 12-11-2018 Through 15-11-2018",
year = "2018",
doi = "10.1007/978-3-030-02922-7\_20",
language = "英语",
isbn = "9783030029210",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "297--307",
editor = "Hye-Young Paik and Hua Wang and Rui Zhou and Hakim Hacid and Wojciech Cellary",
booktitle = "Web Information Systems Engineering – WISE 2018 - 19th International Conference, 2018, Proceedings",
address = "德国",
}