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Parsing Thai Social Data: A New Challenge for Thai NLP

Dependency parsing (DP) is a task that analyzes text for syntactic structure and relationship between words. DP is widely used to improve natural language processing (NLP) applications in many languages such as English. Previous works on DP are generally applicable to formally written languages. However, they do not apply to informal languages such as the ones used in social networks. Therefore, DP has to be researched and explored with such social network data. In this paper, we explore and identify a DP model that is suitable for Thai social network data. After that, we will identify the appropriate linguistic unit as an input. The result showed that, the transition based model called, improve Elkared dependency parser outperform the others at UAS of 81.42%.Comment: 7 Pages, 8 figures, to be published in The 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2019

Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!

Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at \url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201

The modal particle ma 嘛: theoretical frames, analysis and interpretive perspectives

This article sets out to provide a semantic and pragmatic account of the modal particle ma 嘛, endeavouring to put into light new aspects in its function which, at present, remain widely unexplored in the literature. It presents an analysis of the particle ma by interrogating a written and a spoken corpus, showing how the semantic and the pragmatic levels are tightly interweaved in the functioning of ma: the results supported my hypothesis that the particle is plausibly a marker of interpersonal evidentiality (IE), a category set up by Tantucci (2013), used to signal a socially acknowledged piece of information, playing a fundamental role in the expression of politeness by safeguarding the interlocutors’ face; consequently, ma is always used with information that has an active or accessible status in the interlocutors’ mind and that is always pragmatically salient, independently of its position (at the end or inside the sentence), marking a Topic or a Focus. The particle performs pragmatic functions close to the ones of discourse markers since it increases the relevance of the marked information to the context, therefore also playing a contributing role in the coherence of discourse