Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
This work addresses the problem of limited argumentation data for customer reviews, particularly in Chinese, but it is incremental as it applies an existing crowdsourcing method to a new language domain.
The authors tackled the lack of large-scale argumentation corpora for customer reviews by using crowdsourcing to collect annotations in Chinese hotel reviews, resulting in a dataset with 4814 argument component annotations and 411 argument relation annotations that is comparable to existing corpora in other languages.
Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.