CakeResume 找人才
进阶搜寻
On
4 到 6 年
6 到 10 年
10 到 15 年
15 年以上
資料分析師 Data Analyst @Portto 門戶科技| Blocto2022 ~ 2024
・
Data Analyst、Data Engineer、Data Scientist、Customer Experience Analyst
一個月內
陶俊良 (Tao,Chun-Liang) Taipei, Taiwan Email: [email protected] Phone:I am very sensitive to data and enjoy finding inspiration and ideas from them. I am proficient in machine learning, text analysis, and recommendation systems, EVM blockchain analytics, and currently use Python as my primary programming languages. I am always open to learning new things, such as learning new data structure from blockchain. I am currently very interested in blockchain data and on-chain user segamentation. I was working in digital media, advertising (DSP, SSP, DMP platforms), gaming user analyst, blockchain
就职中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
臺灣大學
・
流行病學與預防醫學所 生物統計組
智慧製造全端開發工程師 @聯華電子股份有限公司2022 ~ 现在
・
AI工程師、機器學習工程師、深度學習工程師、影像演算法工程師、資料科學家、Ai Application Engineer,Machine Learning Engineer,Deep Learning Engineer,Data Scientist
一個月內
就职中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
元智大學 Yuan Ze University
・
工業工程與管理學系所曾任
博士後研究員 @洛桑大學神經發育疾病實驗室2023 ~ 2023
・
Data Scientist, Data Analyst, Machine Learning Engineer
一個月內
待业中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
洛桑聯邦理工學院(EPFL)
・
神經科學
曾任
Service Provider @Taron Solutions Limited2023 ~ 2023
・
AI工程師、機器學習工程師、電腦視覺工程師、資料科學家、Machine Learning Engineer、Computer Vision Engineer、Data Scientist
一個月內
李慕全(MuChuan Li) 畢業於國立臺北科技大學資工所，研究領域為深度學習、電腦視覺、及影像處理。在學期間致力於應用電腦視覺技術解決交通問題，擁有多項產學合作的專案開發經驗，亦在電腦視覺領域中發表過多篇學術論文，主要研究主題包含物
待业中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
國立臺北科技大學
・
資訊工程AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
一個月內
就职中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
國立政治大學（National Chengchi University）
・
資訊科學系
曾任
Data Engineer @Rooit Inc. (XO App)2023 ~ 2023
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
一個月內
邱義塵 於獨角獸多媒體設計有限公司擔任 遊戲測試工程師一職 建立公司測試團隊的測試流程和撰寫自動化測試程式 SDET、AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist 城市，TW [email protected] 工作經歷 獨角獸多媒體
待业中
・
正在积极求职中
全职 / 对远端工作有兴趣
6 到 10 年
中國醫藥大學(China Medical University)
・
臨床醫學研究所
OPC Chief Engineer @TSMC2020 ~ 现在
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
一個月內
Chun-Jung Huang [email protected] Chiao-Tung University, Ph.D. - Photonics,2015 ~ 2020 Member of The Phi Tau Phi Scholastic Honor Society of the Republic of China. Work Experience TSMC, OPC Chief Engineer (MarPresent) ◆Introduced image anomaly detection techniques to identify and address defects in photomask manufacturing, significantly improving product quality and reducing turnaround time. ◆Managed large-scale data processing tasks, demonstrating expertise in analyzing and handling datasets of hundreds of millions, to bolster model development and optimization. ◆Excelled in distributed computing, optimizing code execution across thousands of systems to
就职中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
National Chiao-Tung University
・
Ph.D. - Clinical Engineering
Product Manager @東元電機股份有限公司 (TECO Electric & Machinery Co. Ltd.)2023 ~ 2023
・
Data Scientist, Data Analyst, Machine Learning Engineer
一個月內
梁賦康 （Foo-Hong, Leong） Taoyuan City, Taiwan Email: [email protected] Tel:Skills • Languages: Python • DataBases: MySQL, SQLite • Infrastructure tools: Github • Machine learning libraries: TensorFlow, Keras, and Scikit-learn • Data visualization tools: Power BI, Seaborn and Matplotlib • Deployment: Streamlit Summary I have been working in Motor Manufacturing Industry for 8 years. My first programming was going to my Bachelor's degree, C++ was the first program I learned. Then I started to learn Python in 2018 at TEDU and my first project was the Stock Trend Prediction by CNN. I kept
就职中
・
正在积极求职中
全职 / 对远端工作有兴趣
6 到 10 年
國立成功大學 National Cheng Kung University
・
Mechanical Engineering
曾任
Senior Data Analyst @趨勢科技2022 ~ 现在
・
Data Scientist, Data Analyst, Machine Learning Engineer
一個月內
customers by using statistical methods and machine learning methods. Developing automation regular reports, maintaining SQL store procedures, Tableau dashboards and Power BI dashboards. Cooperated with cross-functional team (Product, Marketing, Platform, PM, IT, Sales) to provide timely and accuracy business insight analysis. Developing automated web crawler on MMA website to collect ETF, fund, bond information. Skill : Microsoft SQL Server · Microsoft Power BI · Data Cubes · R · Python · Tableau · Web Crawling · machine learning · IMPALA · HIVE · Git · Docker Data Analyst • Catchplay AprOct 2020 Indonesia OTT customer profile analysis - Collecting, analyzing and evaluating data and campaign performa...
待业中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
輔仁大學 Fu Jen Catholic University
・
統計資訊學系
曾任
Career transition @Career Break2024 ~ 2024
・
NLP Engineer / Data Scientist / Machine Learning Engineer
一個月內
江易倫 Data Scientist | Python | SQL | NLP | GenAI 具備5年以上程式撰寫能力，擅長Python、SQL與Linux 擅長資料清洗、分析與分類貼標 具有自然語言處理與研究經驗 大型語言模型LLM及生成式AI訓練與使用經驗 RAG技術使用與知識庫建立經驗 過往研究專案 中華電信智能標籤案
待业中
・
正在积极求职中
全职 / 对远端工作有兴趣
4 到 6 年
National Chengchi University
・
資訊科學系最轻量、快速的招募方案，数百家企业的选择
搜寻简历，主动联系求职者，提升招募效率。
- 浏览所有搜寻结果
- 每日可无限次数开启陌生对话
- 搜尋僅開放付費企業檢視的简历
- 检视使用者信箱 & 电话
搜寻技巧
1
Search a precise keyword combination
senior backend php
2
Use quotes to search for an exact phrase
"business development"
3
Use the minus sign to eliminate results containing certain words
UI designer -UX
职场能力评价定义
专业技能
该领域中具备哪些专业能力（例如熟悉 SEO 操作，且会使用相关工具）。
问题解决能力
能洞察、分析问题，并拟定方案有效解决问题。
变通能力
遇到突发事件能冷静应对，并随时调整专案、客户、技术的相对优先序。
沟通能力
有效传达个人想法，且愿意倾听他人意见并给予反馈。
时间管理能力
了解工作项目的优先顺序，有效运用时间，准时完成工作内容。
团队合作能力
具有向心力与团队责任感，愿意倾听他人意见并主动沟通协调。
领导力
专注于团队发展，有效引领团队采取行动，达成共同目标。
专业背景
目前状态
就职中
求职阶段
专业
数据科学家
产业
工作年资
小於 1 年
管理经历
无
技能
语言能力
English
・
专业
Chinese
・
母语或双语
求职偏好
希望获得的职位
機器學習工程師
预期工作模式
全职
期望的工作地点
Taipei, 台灣, Kaohsiung City, 台灣
远端工作意愿
对远端工作有兴趣
接案服务
否
学历
学校
Tsinghua University
主修科系
Computer Science
Hsuehkuan Lu
Machine Learning Engineer
Highly focused, cooperative, and with strong learning ability. Passionate about machine learning, natural language processing/understanding, and data science. Practical experiences in ML projects with Python, and GraphQL API design. Proficient in implementing algorithms and researching.
Education
2016 - 2019
Tsinghua University
Master of Science, Computer Science
Knowledge Engineering Group (KEG) Lab
2012 - 2016
National Central University
Bachelor of Science, Computer Science and Information Engineering
Work Experience
Algorithm Engineer • HIPR PacSoft Technologies
August 2020 - Present
- Model MySQL 30+ tables, and Elasticsearch 10+ indices.
- Design GraphQL APIs with Flask, and write unit tests.
- Design NLP data processing pipeline (500M+ data) ranges from Google Scholar, world university rankings, journal rankings to institution rankings.
- Design distributed crawling and computing systems with Dask, and asynchronous data processing methods (3-4x faster).
- Import object-relational mapping of Elasticsearch, improving the organization of search engine indices mappings, and simplifying queries.
Projects
Impactio
- Develop social network applications for academic researchers.
- Design large data pipeline for institutions (750K+), journals (35K+), authors (250M+), and publications (230M+).
- Design distributed crawling and on-the-fly merging systems for Google Scholar profile with Dask (avg. 3-5 MIN per user).
- Design cursor-based pagination method with GraphQL to improve built-in offset-based pagination method by 10%+ efficiency.
- Main backend APIs developer in the project (70%+).
News Miner
- Developed news centering and trend/topic analysis applications.
- Designed English news data processing pipeline (5K+ per day), and topical news clustering with semantic representations.
- Adopted Word2Vec distributed representations with k-means clustering to produce text features, and designed a single-pass clustering method to merge related news.
- Handled 100K+ news in 10 MIN, largely improved the processing performance by 250%-300% comparing to k-means clustering.
POS Tagging and Dependency Parsing
- Combined POS tagging with dependency parsing so as to alleviate the error propagation problems and enriched sequence tagging information across tasks.
- Experimental results on dataset Universal Dependency 2.0 achieved 81.14 with LAS (Labelled Attachment Score) averagely, while baseline scored 72.14 with LAS (11% improvement).
- The ablation tests indicated that the POS tagging information largely improved the performance of dependency parsing by 10%.
- Tagging system was implemented in Tensorflow with Python, and was able to annotate 1K+ sentences per MIN.
Wikipedia Data Processing and Joint Representations of Entities and Texts
- Tackled the parallel corpus reliance on the cross-lingual problems and enriched textual information with external knowledge information.
- Designed a weakly-supervised algorithm to produce aligned cross-lingual corpus (avg. 300K+ paragraphs per language pair).
- Jointly modeled knowledge entities and texts with the model derived from Skip-Gram with Negative Sampling algorithm and made word vectors (300d) public.
- Achieved 44.99 Pearson-r in SemEval-2017 Track 4a task (En-Es), while end-to-end LASER model achieved 40.87 Pearson-r (10% improvement).
- Proposed a cross-lingual information retrievement task, and our proposed method achieved 80% Top-10 Accuracy, while the strongest baseline acquired 61% Top-10 Accuracy (30% significant improvement).
Skills
Programming Languages
Python
C++
Java
Framework and Data Analysis
Tensorflow
PyTorch
Pandas
Jupyter Lab
Dask
GraphQL
Database and Search Engine
MySQL
Elasticsearch
MongoDB
VCS and CI/CD Tools
Git
Bitbucket
Alembic
Jenkins
Publications
- Hsuehkuan Lu, Yixin Cao, Lei Hou, and Juanzi Li. Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity. International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE), 2019. CCIS Volume 1058, pages 425-440.
- Hsuehkuan Lu, Lei Hou, and Juanzi Li. How Important Is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks. Chinese Computational Lingustics (CCL), 2019. LNCS, Volume 11856, pages 625-637.
呂學寬
機器學習工程師
Machine Learning Engineer
專注力強，樂於合作，學習能力強。對機器學習、自然語言處理/理解以及資料科學充滿熱情。擅長以Python做數據分析，實作機器學習專案，以及設計 GraphQL API。具備熟練實作算法模型以及研究能力。
Email: [email protected]
Phone: (+886) 905193233
工作經驗
算法工程師 • HIPR PacSoft Technologies
八月 2020 - Present
- MySQL 30+表架構模型建立，以及 Elasticsearch 10+索引架構模型建立。
- 利用 GraphQL + Flask 設計後端 API，並撰寫單元測試。
- 自然語言處理資料處理流程設計（合計約5億筆資料），包含Google Scholar、世界大學排名、期刊排名，以及學術機構排名資料。
- 設計 Dask 分布式計算、爬蟲（16+ workers），以及 Asyncio 非同步資料處理流程（約提升3-4倍效能）。
專案
Impactio
- 針對學術研究者設計的社交網絡。
- 設計大量資料處理流程：學術機構 (750K+)、期刊 (35K+)、作者 (250M+) 以及學術論文 (230M+)。
- 利用 Dask 設計 Google Scholar 使用者資料分布式爬蟲方法，以及實時合併處理系統 (平均每個使用者 3-5 MIN)。
- 設計 GraphQL cursor-based 分頁方法，優化內建 offset-based 分頁方法，效率提升約 10%+。
- 專案後端 API 服務的主要開發者 (70%+)。
News Miner
- 開發跨新聞媒體及趨勢、主題分析應用。
- 設計英文新聞的資料處理流程 (每日5K+)，以及新聞主題聚類方法設計、文本語義表示學習。
- 採用 Word2Vec 詞向量 + k-means 聚類方法生成文本特徵，並設計單向聚類方法合併相似新聞。
- 系統平均 10 分鐘能處理 100K+ 筆新聞，而採用 k-means 方法則需要 10x 以上的時間才能達到收斂。
詞性標註與依存句法分析
- 結合詞性標註到依存句法任務中，以降低彼此任務間的錯誤信息傳遞，並增強跨任務間的結構信息。
- 在 Universal Dependency 2.0 的數據集上，我們提出的方法達到 81.14 LAS (多語言平均)，baseline 為 72.14 LAS，提出方法提升約 11%。
- 對照實驗顯示詞性信息對於依存句法分析的預測有約 10%+的影響。
- 標注系統由 Tensorflow + Python 實作，處理速度約每分鐘1K+。
維基百科數據處理及聯合知識文本表示學習
- 解決跨語言方法對於平行語料的依賴，並嘗試結合知識實體到文本中以增強文本信息。
- 設計弱監督算法生成維基百科跨語言語料 (平均每語言對 300K+ 段落)。
- 算法基於負採樣 Skip-Gram 算法聯合學習知識實體、文本表示，並公開實驗訓練出的詞向量 (300d)。
- 在 SemEval-2017 Track 4a 跨語言 (En-Es) 文本語義相似度任務中達到 44.99 Pearson-r，對比端到端模型 LASER 40.87 Pearson-r 提升近 10%。
- 提出跨語言信息檢索任務，我們的方法達到 80% Top-10 Accuracy，對比 baseline 實驗 61% 有大幅提升 (30%)。
技能
程式語言
Python
C++
Java
框架及資料分析工具
Tensorflow
PyTorch
Pandas
Jupyter Lab
Dask
GraphQL
資料庫及搜尋引擎
MySQL
Elasticsearch
MongoDB
版控及CI/CD工具
Git
Bitbucket
Alembic
Jenkins
論文發表
- Hsuehkuan Lu, Yixin Cao, Lei Hou, and Juanzi Li. Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity. International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE), 2019. CCIS Volume 1058, pages 425-440.
- Hsuehkuan Lu, Lei Hou, and Juanzi Li. How Important Is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks. Chinese Computational Lingustics (CCL), 2019. LNCS, Volume 11856, pages 625-637.
End
Hsuehkuan Lu
Machine Learning Engineer
Highly focused, cooperative, and with strong learning ability. Passionate about machine learning, natural language processing/understanding, and data science. Practical experiences in ML projects with Python, and GraphQL API design. Proficient in implementing algorithms and researching.
Education
2016 - 2019
Tsinghua University
Master of Science, Computer Science
Knowledge Engineering Group (KEG) Lab
2012 - 2016
National Central University
Bachelor of Science, Computer Science and Information Engineering
Work Experience
Algorithm Engineer • HIPR PacSoft Technologies
August 2020 - Present
- Model MySQL 30+ tables, and Elasticsearch 10+ indices.
- Design GraphQL APIs with Flask, and write unit tests.
- Design NLP data processing pipeline (500M+ data) ranges from Google Scholar, world university rankings, journal rankings to institution rankings.
- Design distributed crawling and computing systems with Dask, and asynchronous data processing methods (3-4x faster).
- Import object-relational mapping of Elasticsearch, improving the organization of search engine indices mappings, and simplifying queries.
Projects
Impactio
- Develop social network applications for academic researchers.
- Design large data pipeline for institutions (750K+), journals (35K+), authors (250M+), and publications (230M+).
- Design distributed crawling and on-the-fly merging systems for Google Scholar profile with Dask (avg. 3-5 MIN per user).
- Design cursor-based pagination method with GraphQL to improve built-in offset-based pagination method by 10%+ efficiency.
- Main backend APIs developer in the project (70%+).
News Miner
- Developed news centering and trend/topic analysis applications.
- Designed English news data processing pipeline (5K+ per day), and topical news clustering with semantic representations.
- Adopted Word2Vec distributed representations with k-means clustering to produce text features, and designed a single-pass clustering method to merge related news.
- Handled 100K+ news in 10 MIN, largely improved the processing performance by 250%-300% comparing to k-means clustering.
POS Tagging and Dependency Parsing
- Combined POS tagging with dependency parsing so as to alleviate the error propagation problems and enriched sequence tagging information across tasks.
- Experimental results on dataset Universal Dependency 2.0 achieved 81.14 with LAS (Labelled Attachment Score) averagely, while baseline scored 72.14 with LAS (11% improvement).
- The ablation tests indicated that the POS tagging information largely improved the performance of dependency parsing by 10%.
- Tagging system was implemented in Tensorflow with Python, and was able to annotate 1K+ sentences per MIN.
Wikipedia Data Processing and Joint Representations of Entities and Texts
- Tackled the parallel corpus reliance on the cross-lingual problems and enriched textual information with external knowledge information.
- Designed a weakly-supervised algorithm to produce aligned cross-lingual corpus (avg. 300K+ paragraphs per language pair).
- Jointly modeled knowledge entities and texts with the model derived from Skip-Gram with Negative Sampling algorithm and made word vectors (300d) public.
- Achieved 44.99 Pearson-r in SemEval-2017 Track 4a task (En-Es), while end-to-end LASER model achieved 40.87 Pearson-r (10% improvement).
- Proposed a cross-lingual information retrievement task, and our proposed method achieved 80% Top-10 Accuracy, while the strongest baseline acquired 61% Top-10 Accuracy (30% significant improvement).
Skills
Programming Languages
Python
C++
Java
Framework and Data Analysis
Tensorflow
PyTorch
Pandas
Jupyter Lab
Dask
GraphQL
Database and Search Engine
MySQL
Elasticsearch
MongoDB
VCS and CI/CD Tools
Git
Bitbucket
Alembic
Jenkins
Publications
- Hsuehkuan Lu, Yixin Cao, Lei Hou, and Juanzi Li. Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity. International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE), 2019. CCIS Volume 1058, pages 425-440.
- Hsuehkuan Lu, Lei Hou, and Juanzi Li. How Important Is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks. Chinese Computational Lingustics (CCL), 2019. LNCS, Volume 11856, pages 625-637.
呂學寬
機器學習工程師
Machine Learning Engineer
專注力強，樂於合作，學習能力強。對機器學習、自然語言處理/理解以及資料科學充滿熱情。擅長以Python做數據分析，實作機器學習專案，以及設計 GraphQL API。具備熟練實作算法模型以及研究能力。
Email: [email protected]
Phone: (+886) 905193233
工作經驗
算法工程師 • HIPR PacSoft Technologies
八月 2020 - Present
- MySQL 30+表架構模型建立，以及 Elasticsearch 10+索引架構模型建立。
- 利用 GraphQL + Flask 設計後端 API，並撰寫單元測試。
- 自然語言處理資料處理流程設計（合計約5億筆資料），包含Google Scholar、世界大學排名、期刊排名，以及學術機構排名資料。
- 設計 Dask 分布式計算、爬蟲（16+ workers），以及 Asyncio 非同步資料處理流程（約提升3-4倍效能）。
專案
Impactio
- 針對學術研究者設計的社交網絡。
- 設計大量資料處理流程：學術機構 (750K+)、期刊 (35K+)、作者 (250M+) 以及學術論文 (230M+)。
- 利用 Dask 設計 Google Scholar 使用者資料分布式爬蟲方法，以及實時合併處理系統 (平均每個使用者 3-5 MIN)。
- 設計 GraphQL cursor-based 分頁方法，優化內建 offset-based 分頁方法，效率提升約 10%+。
- 專案後端 API 服務的主要開發者 (70%+)。
News Miner
- 開發跨新聞媒體及趨勢、主題分析應用。
- 設計英文新聞的資料處理流程 (每日5K+)，以及新聞主題聚類方法設計、文本語義表示學習。
- 採用 Word2Vec 詞向量 + k-means 聚類方法生成文本特徵，並設計單向聚類方法合併相似新聞。
- 系統平均 10 分鐘能處理 100K+ 筆新聞，而採用 k-means 方法則需要 10x 以上的時間才能達到收斂。
詞性標註與依存句法分析
- 結合詞性標註到依存句法任務中，以降低彼此任務間的錯誤信息傳遞，並增強跨任務間的結構信息。
- 在 Universal Dependency 2.0 的數據集上，我們提出的方法達到 81.14 LAS (多語言平均)，baseline 為 72.14 LAS，提出方法提升約 11%。
- 對照實驗顯示詞性信息對於依存句法分析的預測有約 10%+的影響。
- 標注系統由 Tensorflow + Python 實作，處理速度約每分鐘1K+。
維基百科數據處理及聯合知識文本表示學習
- 解決跨語言方法對於平行語料的依賴，並嘗試結合知識實體到文本中以增強文本信息。
- 設計弱監督算法生成維基百科跨語言語料 (平均每語言對 300K+ 段落)。
- 算法基於負採樣 Skip-Gram 算法聯合學習知識實體、文本表示，並公開實驗訓練出的詞向量 (300d)。
- 在 SemEval-2017 Track 4a 跨語言 (En-Es) 文本語義相似度任務中達到 44.99 Pearson-r，對比端到端模型 LASER 40.87 Pearson-r 提升近 10%。
- 提出跨語言信息檢索任務，我們的方法達到 80% Top-10 Accuracy，對比 baseline 實驗 61% 有大幅提升 (30%)。
技能
程式語言
Python
C++
Java
框架及資料分析工具
Tensorflow
PyTorch
Pandas
Jupyter Lab
Dask
GraphQL
資料庫及搜尋引擎
MySQL
Elasticsearch
MongoDB
版控及CI/CD工具
Git
Bitbucket
Alembic
Jenkins
論文發表
- Hsuehkuan Lu, Yixin Cao, Lei Hou, and Juanzi Li. Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity. International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE), 2019. CCIS Volume 1058, pages 425-440.
- Hsuehkuan Lu, Lei Hou, and Juanzi Li. How Important Is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks. Chinese Computational Lingustics (CCL), 2019. LNCS, Volume 11856, pages 625-637.
End