Yongfu Liao

The rapid flow of information and the abundance of text data on the Internet have brought about the urgent demand for the construction of monitoring resources and techniques used for various purposes. To extract facets of information useful for particular domains from such large and dynamically growing corpora requires an unsupervised yet transparent ways of analyzing the textual data. This paper proposed a hybrid collocation analysis as a potential method to retrieve and summarize Taiwan-related topics posted on Weibo and PTT. By grouping collocates of 臺灣 ‘Taiwan’into clusters of topics via either word embeddings clustering or Latent Dirichlet allocation, lists of collocates can be converted to probability distributions such that distances and similarities can be defined and computed. With this method, we conduct a diachronic analysis of the similarity between Weibo and PTT, providing a way to pinpoint when and how the topic similarity between the two rises or falls. A fine-grained view on the grammatical behavior and political implications is attempted, too. This study thus sheds light on alternative explainable routes for future social media listening method on the understanding of cross-strait relationship.

The prevalence of the web has brought about the construction of many large-scale, automatically segmented and tagged corpora, which inevitably introduces errors due to automation and are likely to have negative impacts on downstream tasks. Collocation extraction from Chinese corpora is one such task that is profoundly influenced by the quality of word segmentation. This paper explores methods to mitigate the negative impacts of word segmentation errors on collocation extraction in Chinese. In particular, we experimented with a simple model that aims to combine several association measures linearly to avoid retrieving false collocations resulting from word segmentation errors. The results of the experiment show that this simple model could not differentiate between true collocations and false collocations The 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020) Taipei, Taiwan, September 24–26, 2020. The Association for Computational Linguistics and Chinese Language Processing resulting from word segmentation errors. An ad hoc case study incorporating information from FastText word vectors is also conducted. The results show that collocates resulting from correct and erroneous word segmentation have different profiles in terms of the semantic similarities between the collocates. The incorporation of word vector information to differentiate between true and false collocations is suggested for future work.