Stance Classification on PTT Comments

With the development of social media and online forums, users have grown accustomed to expressing their agreement and disagreement via short texts. Elements that reveal the user’s stance or subjectivity thus becomes an important resource in identifying the user’s position on a given topic. In the current study, we observe comments of an online bulletin board in Taiwan for how people express their stance when responding to other people’s post in Chinese. A lexicon is built based on linguistic analysis and annotation of the data. We performed binary classification task using these linguistic features and was able to reach an average of 71 percent accuracy. A linguistic analysis on the confusion caused in the classification task is done for future work on better accuracy for such task.

Wiktionary and NLP: Improving synonymy networks

International audienceWiktionary, a satellite of the Wikipedia initiative, can be seen as a potential resource for Natural Language Processing. It requires however to be processed before being used efficiently as an NLP resource. After describing the relevant aspects Wiktionary for our purposes, we focus on its structural properties. Then, we describe how we extracted synonymy networks from this resource. We provide an in-depth study of these synonymy networks and compare them to those extracted from traditional resources. Finally, we describe two methods for semiautomatically improving this network by adding missing relations: (i) using a kind of semantic proximity measure; (ii) using translation relations of Wiktionary itself