no code implementations • ACL 2020 • Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Prasad Tadepalli
Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences.
no code implementations • 14 Aug 2019 • Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Rasha Obeidat, Prasad Tadepalli
We present a new local entity disambiguation system.
1 code implementation • NAACL 2019 • Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, Prasad Tadepalli
Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances.
no code implementations • 9 Sep 2018 • Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli
We also study the behavior of the proposed model to provide explanations for the model's decisions.
no code implementations • EMNLP 2018 • Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli
Deep learning models have achieved remarkable success in natural language inference (NLI) tasks.
no code implementations • COLING 2018 • Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Chao Ma, Rasha Obeidat, Prasad Tadepalli
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS).
no code implementations • COLING 2018 • Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, Prasad Tadepalli
We present a novel deep learning architecture to address the cloze-style question answering task.
no code implementations • NAACL 2018 • Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Z. Fern, Oladimeji Farri
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on Natural Language Inference on SNLI
no code implementations • ACL 2016 • Reza Ghaeini, Xiaoli Z. Fern, Liang Huang, Prasad Tadepalli
Traditional event detection methods heavily rely on manually engineered rich features.