Author:
Ting-Hao (Kenneth) Huang
Abstract:
With the massive amount of social media data becoming available, there is a rising interest in automatic metaphor detection and interpretation from open social text. One of the most well-known approaches to this subject is identifying the violation of selectional preference. The basic concept of selectional preference is that verbs tend to have semantic preferences of their arguments and that violations of these preferences are strong indicators of metaphorical language use. Nevertheless, previously, few works have focused on metaphor detection of social media data. In response to this problem, we propose a three-step framework that is based on the technology of selection preference modeling to detect metaphors in social media data. We conduct a pilot study of this framework on the data of a real-world online support group. Furthermore, to improve our approach, we also leverage topical analysis techniques in our framework. As a result, we address the challenges of the task of metaphor detection in social media data, provide qualitative analysis for our experiments, and illustrate our insight based on the results.
Keywords:
Metaphor, Cluster, Selectional Preference, Social Media Data
Author:
Yi-Ching Zeng, Tsun Ku, Shih-Hung Wu, Liang-Pu Chen, and Gwo-Dong Chen
Abstract:
The paper addresses an opinion mining problem: how to find the helpful reviews from online consumer reviews via the quality of the content. Since there are too many reviews, efficiently identifying the helpful ones earlier can benefit both consumers and companies. Consumers can read only the helpful opinions from helpful reviews before they purchase a product, while companies can acquire the true reasons a product is liked or hated. A system is built to assess the difficulty of the problem. The experimental results show that helpful reviews can be distinguished from unhelpful ones with high precision.
Keywords:
Helpful Opinion Mining, Online Consumer Review, Online Customer Reivew, Text Quality
Author:
Shu-Ling Huang, Yu-Ming Hsieh, Su-Chu Lin, and Keh-Jiann Chen
Abstract:
Event classification is one of the crucial tasks in lexical semantic representation. Traditionally, researchers have regarded process and state as two top-level events and discriminated between them by semantic and syntactic characteristics. In this paper, we add cause-result relativity as an auxiliary criterion to discriminate between process and state by structuring about 40,000 Chinese verbs to the two correspondent event hierarchies in E-HowNet. All verbs are classified according to their semantic similarity with the corresponding conceptual types of ontology. As a result, we discover deficiencies of the dichotomy approach and point out that any discrete event classification system is insufficient to make a clear-cut classification for synonyms with slightly different semantic focuses. We then propose a solution to remedy the deficiencies of the dichotomy approach. For the process or state type mismatched verbs, their inherited semantic properties will be adjusted according to their PoS and semantic expressions to preserve their true semantic and syntactic information. Furthermore, cause-result relations will be linked between corresponding processes and states to bridge the gaps of the dichotomy approach.
Keywords:
Event Classification, Process and State, Lexical Representation, Cause-result Relativity between Verbs
Author:
Li-ping Chang
Abstract:
The study aims to explore the salient linguistic features of Chinese lexical items from different L1s learners. The research method is corpus-based, including comparing the learner corpus and the native-speaker corpus, as well as sub-corpora for different L1s. The learner corpus which consists of more than 1.14 million Chinese words from novice proficiency to advanced learners�� texts is mainly from the computer-based writing Test of Chinese as a Foreign Language (TOCFL). The sub-corpora of Japanese, English, Korean, Vietnamese, Indonesia and Thai are observed. Japanese corpus is top 1, which occupies twenty four percent of the total data, followed by English, Korean, and etc. And the native corpus is from the Academia Sinica balanced corpus. Through the overuse or underuse linguistic forms and keyword-keyness analysis, some salient features are discovered. For examples, comparative to Chinese learners with other L1s, English language background learners show the unusual high frequency on pronouns and unusual low frequency on sentential final particles in Chinese writing. And Japanese as well as Korean background learners tend to overuse the post form �𤉙e hua�� instead of �㶥uguo�� when expressing the �𤓎f�� sentence, and overuse �泟uoyi�� instead of �㷳inwei�� when expressing the cause-effect relation. The article also provides possible explanations for these results from the aspects of learners�� native language typology, linguistic structure, syntactic category and culture.
Keywords:
Mandarin Chinese, Learner Corpus, Contrastive Inter-language Analysis , Keyword-keyness, CEFR, Language Transfer
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