Wen-Chao Yeh


2022

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Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora
Wen-Chao Yeh | Yu-Lun Hsieh | Yung-Chun Chang | Wen-Lian Hsu
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

This study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost. Specifically, we compare the performances of Jieba, CKIP, and MONPA on word segmentation, part-of-speech tagging and named entity recognition through extensive experiments. Experimental results show that MONPA using GPU for batch segmentation can greatly reduce the processing time of massive datasets. In addition, its features such as word segmentation, part-of-speech tagging, and named entity recognition are beneficial to downstream applications.

2021

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Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews
Yu-Ya Cheng | Wen-Chao Yeh | Yan-Ming Chen | Yung-Chun Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user’s experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.

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Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network
Yu-Chi Liang | Min-Chen Chen | Wen-Chao Yeh | Yung-Chun Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Machine learning methods for financial document analysis have been focusing mainly on the textual part. However, the numerical parts of these documents are also rich in information content. In order to further analyze the financial text, we should assay the numeric information in depth. In light of this, the purpose of this research is to identify the linking between the target cashtag and the target numeral in financial tweets, which is more challenging than analyzing news and official documents. In this research, we developed a multi model fusion approach which integrates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). We also encode dependency information behind text into the model to derive semantic latent features. The experimental results show that our model can achieve remarkable performance and outperform comparisons.

2019

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MONPA:中文命名實體及斷詞與詞性同步標註系統(MONPA: A Multitask Chinese Segmentation, Named-entity and Part-of-speech Annotator)
Wen-Chao Yeh | Yu-Lun Hsieh | Yung-Chun Chang | Wen-Lian Hsu
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)