Jason S. Chang

Also published as: Jason Chang, Jason J. Chang, Jason J. S. Chang, Jason J.S. Chang, Jason S Chang

2024

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Generative Dictionary: Improving Language Learner Understanding with Contextual Definitions
Kai-Wen Tuan | Hai-Lun Tu | Jason S. Chang
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce GenerativeDictionary, a novel dictionary system that generates word sense interpretations based on the given context. Our approach involves transforming context sentences to highlight the meaning of target words within their specific context. The method involves automatically transforming context sentences into sequences of low-dimensional vector token representations, automatically processing the input embeddings through multiple layers of transformers, and automatically generate the word senses based on the latent representations derived from the context. At runtime, context sentences with target words are processed through a transformer model that outputs the relevant word senses.Blind evaluations on a combined set of dictionary example sentences and generated sentences based on given word senses demonstrate that our method is comparable to traditional word sense disambiguation (WSD) methods. By framing WSD as a generative problem, GenerativeDictionary delivers more precise and contextually appropriate word senses, enhancing the effectiveness of language learning tools.

2023

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Learning to Paraphrase Sentences to Different Complexity Levels
Alison Chi | Li-Kuang Chen | Yi-Chen Chang | Shu-Hui Lee | Jason S. Chang
Transactions of the Association for Computational Linguistics, Volume 11

While sentence simplification is an active research topic in NLP, its adjacent tasks of sentence complexification and same-level paraphrasing are not. To train models on all three tasks, we present two new unsupervised datasets. We compare these datasets, one labeled by a weak classifier and the other by a rule-based approach, with a single supervised dataset. Using these three datasets for training, we perform extensive experiments on both multitasking and prompting strategies. Compared to other systems trained on unsupervised parallel data, models trained on our weak classifier labeled dataset achieve state-of-the-art performance on the ASSET simplification benchmark. Our models also outperform previous work on sentence-level targeting. Finally, we establish how a handful of Large Language Models perform on these tasks under a zero-shot setting.

2022

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Aligning Sentences in a Paragraph-Paraphrased Corpus with New Embedding-based Similarity Measures
Aleksandra Smolka Smolka | Hsin-Min Wang | Jason S. Chang | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 27, Number 2, December 2022

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Is Character Trigram Overlapping Ratio Still the Best Similarity Measure for Aligning Sentences in a Paraphrased Corpus?
Aleksandra Smolka | Hsin-Min Wang | Jason S. Chang | Keh-Yih Su
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

Sentence alignment is an essential step in studying the mapping among different language expressions, and the character trigram overlapping ratio was reported to be the most effective similarity measure in aligning sentences in the text simplification dataset. However, the appropriateness of each similarity measure depends on the characteristics of the corpus to be aligned. This paper studies if the character trigram is still a suitable similarity measure for the task of aligning sentences in a paragraph paraphrasing corpus. We compare several embedding-based and non-embeddings model-agnostic similarity measures, including those that have not been studied previously. The evaluation is conducted on parallel paragraphs sampled from the Webis-CPC-11 corpus, which is a paragraph paraphrasing dataset. Our results show that modern BERT-based measures such as Sentence-BERT or BERTScore can lead to significant improvement in this task.

2021

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Automatic Extraction of English Grammar Pattern Correction Rules
Kuan-Yu Shen | Yi-Chien Lin | Jason S. Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

We introduce a method for generating error-correction rules for grammar pattern errors in a given annotated learner corpus. In our approach, annotated edits in the learner corpus are converted into edit rules for correcting common writing errors. The method involves automatic extraction of grammar patterns, and automatic alignment of the erroneous patterns and correct patterns. At run-time, grammar patterns are extracted from the grammatically correct sentences, and correction rules are retrieved by aligning the extracted grammar patterns with the erroneous patterns. Using the proposed method, we generate 1,499 high-quality correction rules related to 232 headwords. The method can be used to assist ESL students in avoiding grammatical errors, and aid teachers in correcting students’ essays. Additionally, the method can be used in the compilation of collocation error dictionaries and the construction of grammar error correction systems.

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Learning to Find Translation of Grammar Patterns in Parallel Corpus
Kai-Wen Tuan | Yi-Jyun Chen | Yi-Chien Lin | Chun-Ho Kwok | Hai-Lun Tu | Jason S. Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

We introduce a method for assisting English as Second Language (ESL) learners by providing translations of Collins COBUILD grammar patterns(GP) for a given word. In our approach, bilingual parallel corpus is transformed into bilingual GP pairs aimed at providing native language support for learning word usage through GPs. The method involves automatically parsing sentences to extract GPs, automatically generating translation GP pairs from bilingual sentences, and automatically extracting common bilingual GPs. At run-time, the target word is used for lookup GPs and translations, and the retrieved common GPs and their example sentences are shown to the user. We present a prototype phrase search engine, Linggle GPTrans, that implements the methods to assist ESL learners. Preliminary evaluation on a set of more than 300 GP-translation pairs shows that the methods achieve 91% accuracy.

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Identify Bilingual Patterns and Phrases from a Bilingual Sentence Pair
Yi-Jyun Chen | Hsin-Yun Chung | Jason S. Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

This paper presents a method for automatically identifying bilingual grammar patterns and extracting bilingual phrase instances from a given English-Chinese sentence pair. In our approach, the English-Chinese sentence pair is parsed to identify English grammar patterns and Chinese counterparts. The method involves generating translations of each English grammar pattern and calculating translation probability of words from a word-aligned parallel corpora. The results allow us to extract the most probable English-Chinese phrase pairs in the sentence pair. We present a prototype system that applies the method to extract grammar patterns and phrases in parallel sentences. An evaluation on randomly selected examples from a dictionary shows that our approach has reasonably good performance. We use human judge to assess the bilingual phrases generated by our approach. The results have potential to assist language learning and machine translation research.

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Extracting Academic Senses: Towards An Academic Writer’s Dictionary
Hsin-Yun Chung | Li-Kuang Chen | Jason S Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

We present a method for determining intended sense definitions of a given academic word in an academic keyword list. In our approach, the keyword list are converted into unigram of all possible Mandarin translations, intended or not. The method involve converting words in the keyword list into all translations using a bilingual dictionary, computing the unigram word counts of translations, and computing character counts from the word counts. At run-time, each definition (with associated translation) of the given word is scored with word and character counts, and the definition with the highest count is returned. We present a prototype system for the Academic Keyword List to generate definitions and translation for pedagogy purposes. We also experimented with clustering definition embeddings of all words and definitions, and identifying intended sense in favor of embedding in larger clusters. Preliminary evaluation shows promising performance. This endeavor is a step towards creating a full-fledged dictionary from an academic word list.

2020

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Chinese Spelling Check based on Neural Machine Translation
Jhih-Jie Chen | Hai-Lun Tu | Ching-Yu Yang | Chiao-Wen Li | Jason S. Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 25, Number 1, June 2020

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改善詞彙對齊以擷取片語翻譯之方法 (Improving Word Alignment for Extraction Phrasal Translation)
Yi-Jyun Chen | Ching-Yu Helen Yang | Jason S. Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 25, Number 2, December 2020

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LinggleWrite: a Coaching System for Essay Writing
Chung-Ting Tsai | Jhih-Jie Chen | Ching-Yu Yang | Jason S. Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

This paper presents LinggleWrite, a writing coach that provides writing suggestions, assesses writing proficiency levels, detects grammatical errors, and offers corrective feedback in response to user’s essay. The method involves extracting grammar patterns, training models for automated essay scoring (AES) and grammatical error detection (GED), and finally retrieving plausible corrections from a n-gram search engine. Experiments on public test sets indicate that both AES and GED models achieve state-of-the-art performance. These results show that LinggleWrite is potentially useful in helping learners improve their writing skills.

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Improving Phrase Translation Based on Sentence Alignment of Chinese-English Parallel Corpus
Yi-Jyun Chen | Ching-Yu Helen Yang | Jason S. Chang
Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020)

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Email Writing Assistant System
Jason S. Chang | Ching-Yu Yang | Guan-Fu Peng
Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020)

2019

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Level-Up: Learning to Improve Proficiency Level of Essays
Wen-Bin Han | Jhih-Jie Chen | Chingyu Yang | Jason Chang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce a method for generating suggestions on a given sentence for improving the proficiency level. In our approach, the sentence is transformed into a sequence of grammatical elements aimed at providing suggestions of more advanced grammar elements based on originals. The method involves parsing the sentence, identifying grammatical elements, and ranking related elements to recommend a higher level of grammatical element. We present a prototype tutoring system, Level-Up, that applies the method to English learners’ essays in order to assist them in writing and reading. Evaluation on a set of essays shows that our method does assist user in writing.

We introduce a system aimed at improving and expanding second language learners’ English vocabulary. In addition to word definitions, we provide rich lexical information such as collocations and grammar patterns for target words. We present Linggle Booster that takes an article, identifies target vocabulary, provides lexical information, and generates a quiz on target words. Linggle Booster also links named-entity to corresponding Wikipedia pages. Evaluation on a set of target words shows that the method have reasonably good performance in terms of generating useful and information for learning vocabulary.

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漢語及物化的大數據研究(A Data Scientific Study of Transitivization in Chinese)
Wei-Tien Dylan Tsai | Ching-Yu Helen Yang | Ying-Zhu Chen | Jhih-Jie Chen | Jason S. Chang
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

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標註英中同步樣式文法之研究(Annotating Synchronous Grammar Patterns across English and Chinese)
Ching-Yu Helen Yang | Ying-Zhu Chen | Jason S. Chang | Yi-Chien Lin | Wei-Tien Dylan Tsai
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

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Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings
Chia-Fang Ho | Jason Chang | Jhih-Jie Chen | Chingyu Yang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

We present a method for learning bilingual word embeddings in order to support second language (L2) learners in finding recurring phrases and example sentences that match mixed-code queries (e.g., “接受 sentence”) composed of words in both target language and native language (L1). In our approach, mixed-code queries are transformed into target language queries aimed at maximizing the probability of retrieving relevant target language phrases and sentences. The method involves converting a given parallel corpus into mixed-code data, generating word embeddings from mixed-code data, and expanding queries in target languages based on bilingual word embeddings. We present a prototype search engine, x.Linggle, that applies the method to a linguistic search engine for a parallel corpus. Preliminary evaluation on a list of common word-translation shows that the method performs reasonablly well.

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TellMeWhy: Learning to Explain Corrective Feedback for Second Language Learners
Yi-Huei Lai | Jason Chang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present a writing prototype feedback system, TellMeWhy, to provide explanations of errors in submitted essays. In our approach, the sentence with corrections is analyzed to identify error types and problem words, aimed at customizing explanations based on the context of the error. The method involves learning the relation of errors and problem words, generating common feedback patterns, and extracting grammar patterns, collocations and example sentences. At run-time, a sentence with corrections is classified, and the problem word and template are identified to provide detailed explanations. Preliminary evaluation shows that the method has potential to improve existing commercial writing services.

2018

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Chinese Spelling Check based on Neural Machine Translation
Chiao-Wen Li | Jhih-Jie Chen | Jason Chang
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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SmartWrite: Extracting Chinese Lexical Grammar Patterns Using Dependency Parsing
Cheng-Cyuan Peng | Ching-Yu Yang | Jhih-Jie Chen | Jason Chang
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Cool English: a Grammatical Error Correction System Based on Large Learner Corpora
Yu-Chun Lo | Jhih-Jie Chen | Chingyu Yang | Jason Chang
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained by EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.

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LanguageNet: Learning to Find Sense Relevant Example Sentences
Shang-Chien Cheng | Jhih-Jie Chen | Chingyu Yang | Jason Chang
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

In this paper, we present a system, LanguageNet, which can help second language learners to search for different meanings and usages of a word. We disambiguate word senses based on the pairs of an English word and its corresponding Chinese translations in a parallel corpus, UM-Corpus. The process involved performing word alignment, learning vector space representations of words and training a classifier to distinguish words into groups of senses. LanguageNet directly shows the definition of a sense, bilingual synonyms and sense relevant examples.

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節能知識問答機器人 (Energy Saving Knowledge Chatbot) [In Chinese]
Jhih-Jie Chen | Shih-Ying Chang | Tsu-Jin Chiu | Ming-Chiao Tsai | Jason S. Chang
Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018)

2017

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Verb Replacer: An English Verb Error Correction System
Yu-Hsuan Wu | Jhih-Jie Chen | Jason Chang
Proceedings of the IJCNLP 2017, System Demonstrations

According to the analysis of Cambridge Learner Corpus, using a wrong verb is the most common type of grammatical errors. This paper describes Verb Replacer, a system for detecting and correcting potential verb errors in a given sentence. In our approach, alternative verbs are considered to replace the verb based on an error-annotated corpus and verb-object collocations. The method involves applying regression on channel models, parsing the sentence, identifying the verbs, retrieving a small set of alternative verbs, and evaluating each alternative. Our method combines and improves channel and language models, resulting in high recall of detecting and correcting verb misuse.

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Learning Synchronous Grammar Patterns for Assisted Writing for Second Language Learners
Chi-En Wu | Jhih-Jie Chen | Jim Chang | Jason Chang
Proceedings of the IJCNLP 2017, System Demonstrations

In this paper, we present a method for extracting Synchronous Grammar Patterns (SGPs) from a given parallel corpus in order to assisted second language learners in writing. A grammar pattern consists of a head word (verb, noun, or adjective) and its syntactic environment. A synchronous grammar pattern describes a grammar pattern in the target language (e.g., English) and its counterpart in an other language (e.g., Mandarin), serving the purpose of native language support. Our method involves identifying the grammar patterns in the target language, aligning these patterns with the target language patterns, and finally filtering valid SGPs. The extracted SGPs with examples are then used to develop a prototype writing assistant system, called WriteAhead/bilingual. Evaluation on a set of randomly selected SGPs shows that our system provides satisfactory writing suggestions for English as a Second Language (ESL) learners.

2016

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Linggle Knows: A Search Engine Tells How People Write
Jhih-Jie Chen | Hao-Chun Peng | Mei-Cih Yeh | Peng-Yu Chen | Jason Chang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

This paper shows the great potential of incorporating different approaches to help writing. Not only did they solve different kinds of writing problems, but also they complement and reinforce each other to be a complete and effective solution. Despite the extensive and multifaceted feedback and suggestion, writing is not all about syntactically or lexically well-written. It involves contents, structure, the certain understanding of the background, and many other factors to compose a rich, organized and sophisticated text. (e.g., conventional structure and idioms in academic writing). There is still a long way to go to accomplish the ultimate goal. We envision the future of writing to be a joyful experience with the help of instantaneous suggestion and constructive feedback.

We introduce a method for learning to translate out-of-vocabulary (OOV) words. The method focuses on combining sublexical/constituent translations of an OOV to generate its translation candidates. In our approach, wild-card searches are formulated based on our OOV analysis, aimed at maximizing the probability of retrieving OOVs’ sublexical translations from existing resource of machine translation (MT) systems. At run-time, translation candidates of the unknown words are generated from their suitable sublexical translations and ranked based on monolingual and bilingual information. We have incorporated the OOV model into a state-of-the-art MT system and experimental results show that our model indeed helps to ease the negative impact of OOVs on translation quality, especially for sentences containing more OOVs (significant improvement).

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GRASP: Grammar- and Syntax-based Pattern-Finder for Collocation and Phrase Learning
Mei-hua Chen | Chung-chi Huang | Shih-ting Huang | Jason S. Chang
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

2009

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Learning Bilingual Linguistic Reordering Model for Statistical Machine Translation
Han-Bin Chen | Jian-Cheng Wu | Jason S. Chang
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Acquiring Translation Equivalences of Multiword Expressions by Normalized Correlation Frequencies
Ming-Hong Bai | Jia-Ming You | Keh-Jiann Chen | Jason S. Chang
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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WikiSense: Supersense Tagging of Wikipedia Named Entities Based WordNet
Joseph Chang | Richard Tzong-Han Tsai | Jason S. Chang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Review Classification Using Semantic Features and Run-Time Weighting
Chung-chi Huang | Meng-chiech Lee | Zhe-nan Lin | Jason S. Chang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Extending Bilingual WordNet via Hierarchical Word Translation Classification
Tzu-yi Nien | Tsun Ku | Chung-chi Huang | Mei-hua Chen | Jason S. Chang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Fertility-based Source-Language-biased Inversion Transduction Grammar for Word Alignment
Chung-Chi Huang | Jason S. Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 14, Number 1, March 2009

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A Thesaurus-Based Semantic Classification of English Collocations
Chung-Chi Huang | Kate H. Kao | Chiung-Hui Tseng | Jason S. Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 14, Number 3, September 2009

2008

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A Thesaurus-Based Semantic Classification of English Collocations
Chung-chi Huang | Chiung-hui Tseng | Kate H. Kao | Jason S. Chang
Proceedings of the 20th Conference on Computational Linguistics and Speech Processing

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Improving Word Alignment by Adjusting Chinese Word Segmentation
Ming-Hong Bai | Keh-Jiann Chen | Jason S. Chang
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Mining the Web for Domain-Specific Translations
Jian-Cheng Wu | Peter Wei-Huai Hsu | Chiung-Hui Tseng | Jason S. Chang
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

We introduce a method for learning to find domain-specific translations for a given term on the Web. In our approach, the source term is transformed into an expanded query aimed at maximizing the probability of retrieving translations from a very large collection of mixed-code documents. The method involves automatically generating sets of target-language words from training data in specific domains, automatically selecting target words for effectiveness in retrieving documents containing the sought-after translations. At run time, the given term is transformed into an expanded query and submitted to a search engine, and ranked translations are extracted from the document snippets returned by the search engine. We present a prototype, TermMine, which applies the method to a Web search engine. Evaluations over a set of domains and terms show that TermMine outperforms state-of-the-art machine translation systems.

2007

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Learning to Find Transliteration on the Web
Chien-Cheng Wu | Jason S. Chang
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)

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Word Translation Disambiguation via Dependency (利用依存關係之辭彙翻譯)
Meng-Chin Hsiao | Kun-Ju Yang | Jason S. Chang
Proceedings of the 19th Conference on Computational Linguistics and Speech Processing

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Learning to Find English to Chinese Transliterations on the Web
Jian-Cheng Wu | Jason S. Chang
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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FAST – An Automatic Generation System for Grammar Tests
Chia-Yin Chen | Hsien-Chin Liou | Jason S. Chang
Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions

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Computational Analysis of Move Structures in Academic Abstracts
Jien-Chen Wu | Yu-Chia Chang | Hsien-Chin Liou | Jason S. Chang
Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions

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Learning to Parse Bilingual Sentences Using Bilingual Corpus and Monolingual CFG
Chung-Chi Huang | Jason S. Chang
Proceedings of the 18th Conference on Computational Linguistics and Speech Processing

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Sense Extraction and Disambiguation for Chinese Words from Bilingual Terminology Bank
Ming-Hong Bai | Keh-Jiann Chen | Jason S. Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 3, September 2006: Special Issue on Selected Papers from ROCLING XVII

2005

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Web-Based Unsupervised Learning for Query Formulation in Question Answering
Yi-Chia Wang | Jian-Cheng Wu | Tyne Liang | Jason S. Chang
Second International Joint Conference on Natural Language Processing: Full Papers

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FAST：電腦輔助英文文法出題系統 (FAST: Free Assistant of Structural Tests) [In Chinese]
Chia-Yin Chen | Ming Hsien Ko | Tzu-Wei Wu | Jason S. Chang
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing

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利用雙語學術名詞庫抽取中英字詞互譯及詞義解歧 (Sense Extraction and Disambiguation for Chinese Words from Bilingual Terminology Bank) [In Chinese]
Ming-Hong Bai | Keh-Jiann Chen | Jason S. Chang
Proceedings of the 17th Conference on Computational Linguistics and Speech Processing

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Collocational Translation Memory Extraction Based on Statistical and Linguistic Information
Thomas C. Chuang | Jia-Yan Jian | Yu-Chia Chang | Jason S. Chang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 10, Number 3, September 2005: Special Issue on Selected Papers from ROCLING XVI

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Learning Source-Target Surface Patterns for Web-based Terminology Translation
Jian-Cheng Wu | Tracy Lin | Jason S. Chang
Proceedings of the ACL Interactive Poster and Demonstration Sessions

2004

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Subsentential Translation Memory for Computer Assisted Writing and Translation
Jian-Cheng Wu | Thomas C. Chuang | Wen-Chi Shei | Jason S. Chang
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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TANGO: Bilingual Collocational Concordancer
Jia-Yan Jian | Yu-Chia Chang | Jason S. Chang
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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Using the Web as Corpus for Un-supervised Learning in Question Answering
Yi-Chia Wang | Jian-Cheng Wu | Tyne Liang | Jason S. Chang
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

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Collocational Translation Memory Extraction Based on Statistical and Linguistic Information
Jia-Yan Jian | Yu-Chia Chang | Jason S. Chang
Proceedings of the 16th Conference on Computational Linguistics and Speech Processing

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Bilingual Collocation Extraction Based on Syntactic and Statistical Analyses
Chien-Cheng Wu | Jason S. Chang
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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Alignment of bilingual named entities in parallel corpora using statistical model
Chun-Jen Lee | Jason S. Chang | Thomas C. Chuang
Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers

Named entities make up a bulk of documents. Extracting named entities is crucial to various applications of natural language processing. Although efforts to identify named entities within monolingual documents are numerous, extracting bilingual named entities has not been investigated extensively owing to the complexity of the task. In this paper, we describe a statistical phrase translation model and a statistical transliteration model. Under the proposed models, a new method is proposed to align bilingual named entities in parallel corpora. Experimental results indicate that a satisfactory precision rate can be achieved. To enhance the performance, we also describe how to improve the proposed method by incorporating approximate matching and person name recognition. Experimental results show that performance is significantly improved with the enhancement.

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Extraction of name and transliteration in monolingual and parallel corpora
Tracy Lin | Jian-Cheng Wu | Jason S. Chang
Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers

Named-entities in free text represent a challenge to text analysis in Machine Translation and Cross Language Information Retrieval. These phrases are often transliterated into another language with a different sound inventory and writing system. Named-entities found in free text are often not listed in bilingual dictionaries. Although it is possible to identify and translate named-entities on the fly without a list of proper names and transliterations, an extensive list of existing transliterations certainly will ensure high precision rate. We use a seed list of proper names and transliterations to train a Machine Transliteration Model. With the model it is possible to extract proper names and their transliterations in monolingual or parallel corpora with high precision and recall rates.

2003

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Proceedings of Research on Computational Linguistics Conference XV
Jason J. Chang | Hsien-Chin Liou
Proceedings of Research on Computational Linguistics Conference XV

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Word-Transliteration Alignment
Tracy Lin | Chien-Cheng Wu | Jason S. Chang
Proceedings of Research on Computational Linguistics Conference XV

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Bilingual Collocation Extraction Based on Syntactic and Statistical Analyses
Chien-Cheng Wu | Jason S. Chang
Proceedings of Research on Computational Linguistics Conference XV

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Interleaving Text and Punctuations for Bilingual Sub-sentential Alignment
Wen-Chi Hsie | Kevin Yeh | Jason S. Chang | Thomas C. Chuang
ROCLING 2003 Poster Papers

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Using Punctuations and Lengths for Bilingual Sub-sentential Alignment
Wen-Chi Hsien | Kevin Yeh | Jason S. Chang | Thomas C. Chuang
ROCLING 2003 Poster Papers

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TotalRecall: A Bilingual Concordance in National Digital Learning Project - CANDLE
Jian-Cheng Wu | Wen-Chi Shei | Jason S. Chang
ROCLING 2003 Poster Papers

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Unsupervised Word Segmentation Without Dictionary
Jason S. Chang | Tracy Lin
ROCLING 2003 Poster Papers

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Building A Chinese WordNet Via Class-Based Translation Model
Jason S. Chang | Tracy Lin | Geeng-Neng You | Thomas C. Chuang | Ching-Ting Hsieh
International Journal of Computational Linguistics & Chinese Language Processing, Volume 8, Number 2, August 2003

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A Statistical Approach to Chinese-to-English Back-Transliteration
Chun-Jen Lee | Jason S. Chang | Jyh-Shing Roger Jang
Proceedings of the 17th Pacific Asia Conference on Language, Information and Computation

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TotalRecall: A Bilingual Concordance for Computer Assisted Translation and Language Learning
Jian-Cheng Wu | Kevin C. Yeh | Thomas C. Chuang | Wen-Chi Shei | Jason S. Chang
The Companion Volume to the Proceedings of 41st Annual Meeting of the Association for Computational Linguistics

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Acquisition of English-Chinese Transliterated Word Pairs from Parallel-Aligned Texts using a Statistical Machine Transliteration Model
Chun-Jen Lee | Jason S. Chang
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

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Class Based Sense Definition Model for Word Sense Tagging and Disambiguation
Tracy Lin | Jason S. Chang
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Adaptive bilingual sentence alignment
Thomas C. Chuang | G.N. You | Jason Chang
Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers

We present a new approach to the problem of aligning English and Chinese sentences in a bilingual corpus based on adaptive learning. While using length information alone produces surprisingly good results for aligning bilingual French and English sentences with success rates well over 95%, it does not fair as well for the alignment of English and Chinese sentences. The crux of the problem lies in greater variability of lengths and match types of the matched sentences. We propose to cope with such variability via a two-pass scheme under which model parameters can be learned from the data at hand. Experiments show that under the approach bilingual English-Chinese texts can be aligned effectively across diverse domains, genres and translation directions with accuracy rates approaching 99%.

2001

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多篇文件自動摘要系統 (Multi-Document Summarization System) [In Chinese]
Jian-Cheng Shen | Jason S. Chang
Proceedings of Research on Computational Linguistics Conference XIV

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統計式片語翻譯模型(A Statistical Model of Terminology Translation) [In Chinese]
Jason S. Chang | Ta-wei Yu
Proceedings of Research on Computational Linguistics Conference XIV

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統計式片語翻譯模型 (Statistical Translation Model for Phrases) [In Chinese]
Jason S. Chang | David Yu | Chun-Jun Lee
International Journal of Computational Linguistics & Chinese Language Processing, Volume 6, Number 2, August 2001

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An Operator Assisted Call Routing System
Chun-Jen Lee | Jason S. Chang
Proceedings of the 16th Pacific Asia Conference on Language, Information and Computation

1998

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Topical Clustering of MRD Senses Based on Information Retrieval Techniques
Jen Nan Chen | Jason S. Chang
Computational Linguistics, Volume 24, Number 1, March 1998 - Special Issue on Word Sense Disambiguation

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A Concept-based Adaptive Approach to Word Sense Disambiguation
Jen Nan Chen | Jason S. Chang
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Taxonomy and lexical semantics—from the perspective of machine readable dictionary
Jason S. Chang | Sue J. Ker | Mathis H. Chen
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

Machine-readable dictionaries have been regarded as a rich knowledge source from which various relations in lexical semantics can be effectively extracted. These semantic relations have been found useful for supporting a wide range of natural language processing tasks, from information retrieval to interpretation of noun sequences, and to resolution of prepositional phrase attachment. In this paper, we address issues related to problems in building a semantic hierarchy from machine-readable dictionaries: genus disambiguation, discovery of covert categories, and bilingual taxonomy. In addressing these issues, we will discuss the limiting factors in dictionary definitions and ways of eradicating these problems. We will also compare the taxonomy extracted in this way from a typical MRD and that of the WordNet. We argue that although the MRD-derived taxonomy is considerably flatter than the WordNet, it nevertheless provides a functional core for a variety of semantic relations and inferences which is vital in natural language processing.

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A Concept-based Adaptive Approach to Word Sense Disambiguation
Jen Nan Chen | Jason S. Chang
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics