Wen-Lian Hsu

Also published as: Wen-lian Hsu


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.

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)

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On the Robustness of Self-Attentive Models
Yu-Lun Hsieh | Minhao Cheng | Da-Cheng Juan | Wei Wei | Wen-Lian Hsu | Cho-Jui Hsieh
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.

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.

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當代非監督式方法之比較於節錄式語音摘要 (An Empirical Comparison of Contemporary Unsupervised Approaches for Extractive Speech Summarization) [In Chinese]
Shih-Hung Liu | Kuan-Yu Chen | Kai-Wun Shih | Berlin Chen | Hsin-Min Wang | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 1, June 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.

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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運用序列到序列生成架構於重寫式自動摘要(Exploiting Sequence-to-Sequence Generation Framework for Automatic Abstractive Summarization)[In Chinese]
Yu-Lun Hsieh | Shih-Hung Liu | Kuan-Yu Chen | Hsin-Min Wang | Wen-Lian Hsu | Berlin Chen
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

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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

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探究新穎語句模型化技術於節錄式語音摘要 (Investigating Novel Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]
Shih-Hung Liu | Kuan-Yu Chen | Yu-Lun Hsieh | Berlin Chen | Hsin-Min Wang | Wen-Lian Hsu
Proceedings of the 26th Conference on Computational Linguistics and Speech Processing (ROCLING 2014)

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Joint Learning of Entity Linking Constraints Using a Markov-Logic Network
Hong-Jie Dai | Richard Tzong-Han Tsai | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 1, March 2014

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Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization
Kuan-Yu Chen | Shih-Hung Liu | Berlin Chen | Ea-Ee Jan | Hsin-Min Wang | Wen-Lian Hsu | Hsin-Hsi Chen
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Proceedings of the 25th Conference on Computational Linguistics and Speech Processing (ROCLING 2013)
Hung-Duen Yang | Wen-Lian Hsu | Chia-Ping Chen
Proceedings of the 25th Conference on Computational Linguistics and Speech Processing (ROCLING 2013)

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改良語句模型技術於節錄式語音摘要之研究 (Improved Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]
Shih-Hung Liu | Kuan-Yu Chen | Hsin-Min Wang | Wen-Lian Hsu | Berlin Chen
Proceedings of the 25th Conference on Computational Linguistics and Speech Processing (ROCLING 2013)

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Sinica-IASL Chinese spelling check system at Sighan-7
Ting-Hao Yang | Yu-Lun Hsieh | Yu-Hsuan Chen | Michael Tsang | Cheng-Wei Shih | Wen-Lian Hsu
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing

2012

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Cost-benefit Analysis of Two-Stage Conditional Random Fields based English-to-Chinese Machine Transliteration
Chan-Hung Kuo | Shih-Hung Liu | Mike Tian-Jian Jiang | Cheng-Wei Lee | Wen-Lian Hsu
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

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Enhancement of Feature Engineering for Conditional Random Field Learning in Chinese Word Segmentation Using Unlabeled Data
Mike Tian-Jian Jiang | Cheng-Wei Shih | Ting-Hao Yang | Chan-Hung Kuo | Richard Tzong-Han Tsai | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 3, September 2012

2011

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Entity Disambiguation Using a Markov-Logic Network
Hong-Jie Dai | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of 5th International Joint Conference on Natural Language Processing

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Text Patterns and Compression Models for Semantic Class Learning
Chung-Yao Chuang | Yi-Hsun Lee | Wen-Lian Hsu
Proceedings of 5th International Joint Conference on Natural Language Processing

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English-to-Chinese Machine Transliteration using Accessor Variety Features of Source Graphemes
Mike Tian-Jian Jiang | Chan-Hung Kuo | Wen-Lian Hsu
Proceedings of the 3rd Named Entities Workshop (NEWS 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)

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Evaluation via Negativa of Chinese Word Segmentation for Information Retrieval
Mike Tian-Jian Jiang | Cheng-Wei Shih | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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Iteratively Estimating Pattern Reliability and Seed Quality With Extraction Consistency
Yi-Hsun Lee | Chung-Yao Chuang | Wen-Lian Hsu
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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Unsupervised Overlapping Feature Selection for Conditional Random Fields Learning in Chinese Word Segmentation
Ting-hao Yang | Tian-Jian Jiang | Chan-hung Kuo | Richard Tzong-han Tsai | Wen-lian Hsu
Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing (ROCLING 2011)

2010

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Term Contributed Boundary Feature using Conditional Random Fields for Chinese Word Segmentation Task
Tian-Jian Jiang | Shih-Hung Liu | Cheng-Lung Sung | Wen-Lian Hsu
Proceedings of the 22nd Conference on Computational Linguistics and Speech Processing (ROCLING 2010)

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Term Contributed Boundary Tagging by Conditional Random Fields for SIGHAN 2010 Chinese Word Segmentation Bakeoff
Tian-Jian Jiang | Shih-Hung Liu | Cheng-Lung Sung | Wen-Lian Hsu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Global Ranking via Data Fusion
Hong-Jie Dai | Po-Ting Lai | Richard Tzong-Han Tsai | Wen-Lian Hsu
Coling 2010: Posters

2008

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Exploring Shallow Answer Ranking Features in Cross-Lingual and Monolingual Factoid Question Answering
Cheng-Wei Lee | Yi-Hsun Lee | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 13, Number 1, March 2008: Special Issue on Cross-Lingual Information Retrieval and Question Answering

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Learning Patterns from the Web to Translate Named Entities for Cross Language Information Retrieval
Yu-Chun Wang | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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Korean-Chinese Cross-Language Information Retrieval Based on Extension of Dictionaries and Transliteration
Yu-Chun Wang | Tzong-Han Richard Tsai | Hsu-Chun Yen | Wen-Lian Hsu
Proceedings of the 19th Conference on Computational Linguistics and Speech Processing

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Korean-Chinese Person Name Translation for Cross Language Information Retrieval
Yu-Chun Wang | Yi-Hsun Lee | Chu-Cheng Lin | Tzong-Han Richard Tsai | Wen-Lian Hsu
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation

2006

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On Closed Task of Chinese Word Segmentation: An Improved CRF Model Coupled with Character Clustering and Automatically Generated Template Matching
Richard Tzong-Han Tsai | Hsieh-Chuan Hung | Cheng-Lung Sung | Hong-Jie Dai | Wen-Lian Hsu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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On Using Ensemble Methods for Chinese Named Entity Recognition
Chia-Wei Wu | Shyh-Yi Jan | Richard Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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A Semi-Automatic Method for Annotating a Biomedical Proposition Bank
Wen-Chi Chou | Richard Tzong-Han Tsai | Ying-Shan Su | Wei Ku | Ting-Yi Sung | Wen-Lian Hsu
Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006

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BIOSMILE: Adapting Semantic Role Labeling for Biomedical Verbs:
Richard Tzong-Han Tsai | Wen-Chi Chou | Yu-Chun Lin | Cheng-Lung Sung | Wei Ku | Ying-Shan Su | Ting-Yi Sung | Wen-Lian Hsu
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology

2005

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Exploiting Full Parsing Information to Label Semantic Roles Using an Ensemble of ME and SVM via Integer Linear Programming
Tzong-Han Tsai | Chia-Wei Wu | Yu-Chun Lin | Wen-Lian Hsu
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Using Maximum Entropy to Extract Biomedical Named Entities without Dictionaries
Tzong-Han Tsai | Chia-Wei Wu | Wen-Lian Hsu
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

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Applying Maximum Entropy to Robust Chinese Shallow Parsing
Shih-Hung Wu | Cheng-Wei Shih | Chia-Wei Wu | Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing

2004

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Applying Meaningful Word-Pair Identifier to the Chinese Syllable-to-Word Conversion Problem
Jia-Lin Tsai | Tien-Jien Chiang | Wen-Lian Hsu
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

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The Construction of a Chinese Named Entity Tagged Corpus: CNEC1.0
Cheng-Wei Shih | Tzong-Han Tsai | Shih-Hung Wu | Chiu-Chen Hsieh | Wen-Lian Hsu
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

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Auto-Generation of NVEF Knowledge in Chinese
Jia-Lin Tsai | Gladys Hsieh | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 9, Number 1, February 2004: Special Issue on Selected Papers from ROCLING XV

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Mencius: A Chinese Named Entity Recognizer Using the Maximum Entropy-based Hybrid Model
Tzong-Han Tsai | Shih-Hung Wu | Cheng-Wei Lee | Cheng-Wei Shih | Wen-Lian Hsu
International Journal of Computational Linguistics & Chinese Language Processing, Volume 9, Number 1, February 2004: Special Issue on Selected Papers from ROCLING XV

2003

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Text Categorization Using Automatically Acquired Domain Ontology
Shih-Hung Wu | Tzong-Han Tsai | Wen-Lian Hsu
Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages

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Auto-Discovery of NVEF Word-Pairs in Chinese
Jia-Lin Tsai | Gladys Hsieh | Wen-Lian Hsu
Proceedings of Research on Computational Linguistics Conference XV

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Chinese Word Auto-Confirmation Agent
Jia-Lin Tsai | Cheng-Lung Sung | Wen-Lian Hsu
Proceedings of Research on Computational Linguistics Conference XV

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Mencius: A Chinese Named Entity Recognizer Using Hybrid Model
Tzong-Han Tsai | Shih-Hung Wu | Wen-Lian Hsu
Proceedings of Research on Computational Linguistics Conference XV

2002

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Applying an NVEF Word-Pair Identifier to the Chinese Syllable-to-Word Conversion Problem
Jia-Lin Tsai | Wen-Lian Hsu
COLING 2002: The 19th International Conference on Computational Linguistics

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SOAT: A Semi-Automatic Domain Ontology Acquisition Tool from Chinese Corpus
Shih-Hung Wu | Wen-Lian Hsu
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes

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Word Sense Disambiguation and Sense-Based NV Event Frame Identifier
Jia-Lin Tsai | Wen-Lian Hsu | Jeng-Woei Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 7, Number 1, February 2002: Special Issue on HowNet and Its Applications

1998

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結合統計與規則的多層次中文斷詞系統 (A hierarchical Chinese word segmentation system based on statistical and rule-based methods) [In Chinese]
Chung-Chen Chen | Wen-Lian Hsu
ROCLING 1998 Short Papers