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Text correction systems (e.g., spell checkers) have been used to improve the quality of computerized text by detecting and correcting errors. However, the task of performing spelling correction and word normalization (text correction) for Thai social media text has remained largely unexplored. In this paper, we investigated how current text correction systems perform on correcting errors and word variances in Thai social texts and propose a method designed for this task. We have found that currently available Thai text correction systems are insufficiently robust for correcting spelling errors and word variances, while the text correctors designed for English grammatical error correction suffer from overcorrections (text rewrites). Thus, we proposed a neural-based text corrector with a two-stage structure to alleviate issues of overcorrections while exploiting the benefits of a neural Seq2Seq corrector. Our method consists of a neuralbased error detector and a Seq2Seq neural error corrector with contextual attention. This novel architecture allows the Seq2Seq network to produce corrections based on both the erroneous text and its context without the need for an end-to-end structure. Our method outperformed all the other evaluated text correction systems. When compared to the second-best result (copy-augmented transformer), our method further reduced the word error rate (WER) from 2.51% to 2.07%, improved the generalized language evaluation understanding (GLEU) score from 0.9409 to 0.9502 on the Thai text correction task, and improved the GLEU score from 0.7409 to 0.7539 on the English spelling correction task.

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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%.

The TJP system is presented, which participated in SemEval 2014 Task 9, Part A: Contextual Polarity Disambiguation. Our system is 'constrained', using only data provided by the organizers. The goal of this task is to identify whether marking contexts are positive, negative or neutral. Our system uses a support vector machine, with extensive pre-processing and achieved an overall F-score of 81.96%.