Papers by Chen-Tse Tsai
Identifying Named Entities as they are Typed (2021.eacl-main)
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| Challenge: | Named Entity Recognition (NER) systems are not applicable to systems that process text in real time as the text is typed. |
| Approach: | They propose a new experimental setup for evaluating Named Entity Recognition systems that evaluates named entities as they are typed on a sentence level . they propose to adapt existing evaluation setups to suit the new setup . |
| Outcome: | The proposed setup shows that the best systems that are evaluated on each token after its typed reach performance within 1–5 F1 points of systems that were evaluated at the end of the sentence. |
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is the backbone of many NLP solutions. |
| Approach: | They propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. |
| Outcome: | The proposed model achieves better precision-recall trade-off at various precision levels. |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
Zero-Shot Open Entity Typing as Type-Compatible Grounding (D18-1)
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| Challenge: | Existing approaches to entity typing have limited flexibility to transfer across text genres and generalize to new type taxonomies. |
| Approach: | They propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. |
| Outcome: | The proposed system outperforms state-of-the-art supervised NER systems on a broad range of datasets and on 'biological domain' it is competitive with supervised systems and outperformed on out-of training datasets. |