Tuan Tran

Also published as: Tuan Dung Tran


2018

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A Trio Neural Model for Dynamic Entity Relatedness Ranking
Tu Nguyen | Tuan Tran | Wolfgang Nejdl
Proceedings of the 22nd Conference on Computational Natural Language Learning

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity relations are very dynamic over time. In this work, we propose a neural network-based approach that leverages public attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.

2017

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Efficient Benchmarking of NLP APIs using Multi-armed Bandits
Gholamreza Haffari | Tuan Dung Tran | Mark Carman
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Comparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers. A rigorous approach involves setting up a hypothesis testing scenario using the performance of the systems on query documents. However, often the hypothesis testing approach needs to send a lot of document queries to the systems, which can be problematic. In this paper, we present an effective alternative based on the multi-armed bandit (MAB). We propose a hierarchical generative model to represent the uncertainty in the performance measures of the competing systems, to be used by Thompson Sampling to solve the resulting MAB. Experimental results on both synthetic and real data show that our approach requires significantly fewer queries compared to the standard benchmarking technique to identify the best system according to F-measure.

2015

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Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information
Tuan Tran | Nam Khanh Tran | Asmelash Teka Hadgu | Robert Jäschke
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing