Yung-Chun Chang


2023

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Fine-Grained Argument Understanding with BERT Ensemble Techniques: A Deep Dive into Financial Sentiment Analysis
Eugene Sy | Tzu-Cheng Peng | Shih-Hsuan Huang | Heng-Yu Lin | Yung-Chun Chang
Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)

2022

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Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
Yung-Chun Chang | Yi-Chin Huang
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 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)

2017

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MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network
Yu-Lun Hsieh | Yung-Chun Chang | Yi-Jie Huang | Shu-Hao Yeh | Chun-Hung Chen | Wen-Lian Hsu
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Part-of-speech (POS) tagging and named entity recognition (NER) are crucial steps in natural language processing. In addition, the difficulty of word segmentation places additional burden on those who intend to deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an end-to-end model using character-based recurrent neural network (RNN) to jointly accomplish segmentation, POS tagging and NER of a Chinese sentence. Experiments on previous word segmentation and NER datasets show that a single model with the proposed architecture is comparable to those trained specifically for each task, and outperforms freely-available softwares. Moreover, we provide a web-based interface for the public to easily access this resource.

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Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory
Yu-Lun Hsieh | Yung-Chun Chang | Nai-Wen Chang | Wen-Lian Hsu
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In this paper, we propose a recurrent neural network model for identifying protein-protein interactions in biomedical literature. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that our approach significantly surpasses state-of-the-art methods with relative improvements of 10% and 18%, respectively. Cross-corpus evaluation also demonstrate that the proposed model remains robust despite using different training data. These results suggest that RNN can effectively capture semantic relationships among proteins as well as generalizes over different corpora, without any feature engineering.

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CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases
Zheng-Wen Lin | Yung-Chun Chang | Chen-Ann Wang | Yu-Lun Hsieh | Wen-Lian Hsu
Proceedings of the IJCNLP 2017, Shared Tasks

Sentiment lexicon is very helpful in dimensional sentiment applications. Because of countless Chinese words, developing a method to predict unseen Chinese words is required. The proposed method can handle both words and phrases by using an ADVWeight List for word prediction, which in turn improves our performance at phrase level. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese phrases. The Mean Absolute Error (MAE) and Pearson’s Correlation Coefficient (PCC) for Valence are 0.723 and 0.835, respectively, and those for Arousal are 0.914 and 0.756, respectively.

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Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
Jitendra Jonnagaddala | Hong-Jie Dai | Yung-Chun Chang
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

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Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
Yi-Jie Huang | Chu Hsien Su | Yi-Chun Chang | Tseng-Hsin Ting | Tzu-Yuan Fu | Rou-Min Wang | Hong-Jie Dai | Yung-Chun Chang | Jitendra Jonnagaddala | Wen-Lian Hsu
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper’s pregnancy stage. Another interesting application is to use the social media platforms to analyze users’ health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.

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Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model
Neha Warikoo | Yung-Chun Chang | Wen-Lian Hsu
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

In this work, we introduce a novel feature engineering approach named “algebraic invariance” to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning. Preprocessing of the training data classifies the entity pairs as either related or unrelated to build instance types for both inter-sentential and intra-sentential scenarios. An invariant function is proposed to process and optimally cluster similar patterns for both positive and negative instances. The learning model for CDR pairs is based on the SVM tree kernel approach, which generates feature trees and vectors and is modeled on suitable invariance based patterns, bringing brevity, precision and context to the identifier features. Results demonstrate that our method outperformed other compared approaches, achieved a high recall rate of 85.08%, and averaged an F1-score of 54.34% without the use of any additional knowledge bases.

2016

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How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles
Yu-Lun Hsieh | Yung-Chun Chang | Chun-Han Chu | Wen-Lian Hsu
Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media

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Linguistic Template Extraction for Recognizing Reader-Emotion
Yung-Chun Chang | Chun-Han Chu | Chien Chin Chen | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 1, June 2016

2015

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Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance
Yung-Chun Chang | Cen-Chieh Chen | Yu-Lun Hsieh | Chien Chin Chen | Wen-Lian Hsu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Semantic Frame-based Statistical Approach for Topic Detection
Yung-Chun Chang | Yu-Lun Hsieh | Cen-Chieh Chen | Chad Liu | Chun-Hung Lu | Wen-Lian Hsu
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

2011

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Robustness Analysis of Adaptive Chinese Input Methods
Mike Tian-Jian Jiang | Cheng-Wei Lee | Chad Liu | Yung-Chun Chang | Wen-Lian Hsu
Proceedings of the Workshop on Advances in Text Input Methods (WTIM 2011)