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Your search keyword '"Zhou, Ben"' showing total 17 results
Start Over Author "Zhou, Ben" Publisher association for computational linguistics
17 results on '"Zhou, Ben"'

4. RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios

Author

Du, Xinya, primary, Zhang, Zixuan, additional, Li, Sha, additional, Yu, Pengfei, additional, Wang, Hongwei, additional, Lai, Tuan, additional, Lin, Xudong, additional, Wang, Ziqi, additional, Liu, Iris, additional, Zhou, Ben, additional, Wen, Haoyang, additional, Li, Manling, additional, Hannan, Darryl, additional, Lei, Jie, additional, Kim, Hyounghun, additional, Dror, Rotem, additional, Wang, Haoyu, additional, Regan, Michael, additional, Zeng, Qi, additional, Lyu, Qing, additional, Yu, Charles, additional, Edwards, Carl, additional, Jin, Xiaomeng, additional, Jiao, Yizhu, additional, Kazeminejad, Ghazaleh, additional, Wang, Zhenhailong, additional, Callison-Burch, Chris, additional, Bansal, Mohit, additional, Vondrick, Carl, additional, Han, Jiawei, additional, Roth, Dan, additional, Chang, Shih-Fu, additional, Palmer, Martha, additional, and Ji, Heng, additional

Published
2022
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11. RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System

Author

Wen, Haoyang, primary, Lin, Ying, additional, Lai, Tuan, additional, Pan, Xiaoman, additional, Li, Sha, additional, Lin, Xudong, additional, Zhou, Ben, additional, Li, Manling, additional, Wang, Haoyu, additional, Zhang, Hongming, additional, Yu, Xiaodong, additional, Dong, Alexander, additional, Wang, Zhenhailong, additional, Fung, Yi, additional, Mishra, Piyush, additional, Lyu, Qing, additional, Surís, Dídac, additional, Chen, Brian, additional, Brown, Susan Windisch, additional, Palmer, Martha, additional, Callison-Burch, Chris, additional, Vondrick, Carl, additional, Han, Jiawei, additional, Roth, Dan, additional, Chang, Shih-Fu, additional, and Ji, Heng, additional

Published
2021
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13. Evaluating Models’ Local Decision Boundaries via Contrast Sets

Author

Gardner, Matt, primary, Artzi, Yoav, additional, Basmov, Victoria, additional, Berant, Jonathan, additional, Bogin, Ben, additional, Chen, Sihao, additional, Dasigi, Pradeep, additional, Dua, Dheeru, additional, Elazar, Yanai, additional, Gottumukkala, Ananth, additional, Gupta, Nitish, additional, Hajishirzi, Hannaneh, additional, Ilharco, Gabriel, additional, Khashabi, Daniel, additional, Lin, Kevin, additional, Liu, Jiangming, additional, Liu, Nelson F., additional, Mulcaire, Phoebe, additional, Ning, Qiang, additional, Singh, Sameer, additional, Smith, Noah A., additional, Subramanian, Sanjay, additional, Tsarfaty, Reut, additional, Wallace, Eric, additional, Zhang, Ally, additional, and Zhou, Ben, additional

Published
2020
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14. CogcompnLP: Your Swiss army knife for NLP

Author

Khashabi, Daniel, Sammons, Mark, Zhou, Ben, Redman, Tom, Christodoulopoulos, Christos E., Srikumar, Vivek, Rizzolo, Nicholas, Luo, Lev Ratinov Guanheng, Do, Quang, Tsai, Chentse, Roy, Subhro, Mayhew, Stephen, Feng, Zhili, Wieting, John, Yu, Xiaodong, Song, Yangqiu, Gupta, Shashank, Upadhyay, Shyam, Arivazhagan, Naveen, Ning, Qiang, Ling, Shaoshi, Roth, Dan, Khashabi, Daniel, Sammons, Mark, Zhou, Ben, Redman, Tom, Christodoulopoulos, Christos E., Srikumar, Vivek, Rizzolo, Nicholas, Luo, Lev Ratinov Guanheng, Do, Quang, Tsai, Chentse, Roy, Subhro, Mayhew, Stephen, Feng, Zhili, Wieting, John, Yu, Xiaodong, Song, Yangqiu, Gupta, Shashank, Upadhyay, Shyam, Arivazhagan, Naveen, Ning, Qiang, Ling, Shaoshi, and Roth, Dan

Abstract

Implementing a Natural Language Processing (NLP) system requires considerable engineering effort: creating data-structures to represent language constructs; reading corpora annotations into these data-structures; applying off-the-shelf NLP tools to augment the text representation; extracting features and training machine learning components; conducting experiments and computing performance statistics; and creating the end-user application that integrates the implemented components. While there are several widely used NLP libraries, each provides only partial coverage of these various tasks. We present our library COGCOMPNLP which simplifies the process of design and development of NLP applications by providing modules to address different challenges: we provide a corpus-reader module that supports popular corpora in the NLP community, a module for various low-level data-structures and operations (such as search over text), a module for feature extraction, and an extensive suite of annotation modules for a wide range of semantic and syntactic tasks. These annotation modules are all integrated in a single system, PIPELINE, which allows users to easily use the annotators with simple direct calls using any JVM-based language, or over a network. The sister project COGCOMPNLPY enables users to access the annotators with a Python interface. We give a detailed account of our system's structure and usage, and where possible, compare it with other established NLP frameworks. We report on the performance, including time and memory statistics, of each component on a selection of well-established datasets. Our system is publicly available for research use and external contributions, at: http://github.com/CogComp/cogcomp-nlp. © LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.

Published
2019

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