Publications

• Jonas Golde, Nicolaas Paul Jedema, RaviKiran Krishnan, Phong Le, Hierarchical Text Classification with LLM-Refined Taxonomies, EACL, 2026.

• Nabila P.R. Siregar, Phong Le, Raquel G. Alhama, An emergent communication framework for honeybee waggle dance, EVOLANG, 2026.

• Adam Kuca, Phong Le, Nguyen Dang, Automated Constraint Model Modification with Large Language Models, LLM-Solve@CP Workshop, 2025.

• Tai Nguyen, Phong Le, Carola Doerr, and Nguyen Dang. Multi-parameter Control for the (1+(λ, λ))-GA on OneMax via Deep Reinforcement Learning. FOGA. 2025

• Tai Nguyen, Phong Le, André Biedenkapp, Carola Doerr, and Nguyen Dang. On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GA. GECCO. 2025

• Daniel Akkerman, Phong Le, and Raquel G. Alhama. The Emergence of Compositional Languages in Multi-entity Referential Games: from Image to Graph Representations. EMNLP. 2024

• Thy Thy Tran, Phong Le, and Sophia Ananiadou. One-shot to Weakly-Supervised Relation Classification using Language Models. AKBC. 2021

• Thy Thy Tran, Phong Le, and Sophia Ananiadou. Revisiting Unsupervised Relation Extraction. ACL. 2020

• Phong Le and Willem Zuidema. DoLFIn: Distributions over Latent Features for Interpretability. COLING. 2020

• Phong Le and Ivan Titov. Boosting Entity Linking Performance by Leveraging Unlabeled Documents. ACL. 2019
• Phong Le and Ivan Titov. Distant Learning for Entity Linking with Automatic Noise Detection. ACL. 2019

• Willem Zuidema and Phong Le. Vector-Based and Neural Models of Semantics. Human Language: From Genes and Brains to Behavior. 2019

• Phong Le and Ivan Titov. Improving Entity Linking by Modeling Latent Relations between Mentions. ACL. 2018

• Phong Le and Ivan Titov. Optimizing Differentiable Relaxations of Coreference Evaluation Metrics. CoNLL. 2017

• Phong Le. Learning Vector Representations for Sentences-The Recursive Deep Learning Approach. PhD Thesis. University of Amsterdam. 2016
• Phong Le and Willem Zuidema. Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive LSTMs. ACL Workshop on Representation Learning for NLP. 2016

• Phong Le, Marc Dymetman, and Jean-Michel Renders. LSTM-Based Mixture-of-Experts for Knowledge-Aware Dialogues. ACL Workshop on Representation Learning for NLP. 2016

• Phong Le and Willem Zuidema. Compositional Distributional Semantics with Long Short Term Memory. *SEM. 2015
• Phong Le. Enhancing the Inside-Outside Recursive Neural Network Reranker for Dependency Parsing. IWPT. 2015
• Phong Le and Willem Zuidema. The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization. EMNLP. 2015

• Phong Le and Willem Zuidema. Unsupervised Dependency Parsing: Let`s Use Supervised Parsers. NAACL. 2015

• Phong Le and Willem Zuidema. Inside-outside semantics: A framework for neural models of semantic composition. NIPS 2014 Workshop on Deep Learning and Representation Learning. 2014

• Phong Le and Willem Zuidema. The Inside-Outside Recursive Neural Network model for Dependency Parsing. EMNNLP. 2014

• Phong Le, Willem Zuidema, and Remko Scha. Learning from errors: Using vector-based compositional semantics for parse reranking. ACL Workshop on Continuous Vector Space Models and their Compositionality. 2013

• Phong Le and Willem Zuidema. Learning Compositional Semantics for Open Domain Semantic Parsing. COLING. 2012

• Phong Le. Learning Semantic Parsing. MSc Thesis. University of Amsterdam. 2012

• Phong Le, Anh Duc Duong, Hai Quan Vu, and Nam Trung Pham. Adaptive Hybrid Mean Shift and Particle Filter. IEEE-RIVF. 2009