K
Kai-Wei Chang
Researcher at University of California, Los Angeles
Publications - 262
Citations - 23031
Kai-Wei Chang is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Word embedding. The author has an hindex of 42, co-authored 183 publications receiving 17271 citations. Previous affiliations of Kai-Wei Chang include Boston University & Amazon.com.
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Few-Shot Representation Learning for Out-Of-Vocabulary Words.
TL;DR: A novel hierarchical attention network-based embedding framework is proposed to serve as the neural regression function, in which the context information of a word is encoded and aggregated from K observations to predict an oracle embedding vector based on limited contexts.
Proceedings ArticleDOI
Towards Understanding Gender Bias in Relation Extraction
Andrew Gaut,Tony Sun,Shirlyn Tang,Yuxin Huang,Jing Qian,Mai ElSherief,Jieyu Zhao,Diba Mirza,Elizabeth Belding,Kai-Wei Chang,William Yang Wang +10 more
TL;DR: In this paper, the authors created WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems.
Proceedings ArticleDOI
Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing
TL;DR: This paper explored several types of corpus linguistic statistics and compiled them into corpus-statistics constraints to facilitate the inference procedure, and proposed new algorithms that adapt two techniques, Lagrangian relaxation and posterior regularization, for cross-lingual dependency parsing.
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Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
Yichao Zhou,Yu Yan,Rujun Han,J. Harry Caufield,Kai-Wei Chang,Yizhou Sun,Peipei Ping,Wei Wang +7 more
TL;DR: A novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level, and significantly outperforms baseline methods for temporal relation extraction.
Proceedings Article
Illinois-Coref: The UI System in the CoNLL-2012 Shared Task
TL;DR: Improvements of Illinois-Coref system from last year are presented, focusing on improving mention detection and pronoun coreference resolution, and a new learning protocol is presented.