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HamleDT: To Parse or Not to Parse?

2012-05-01 · LREC 2012 5 · Daniel Zeman, David Mare{\v{c}}ek, Martin Popel, Loganathan Ramasamy, Jan {\v{S}}t{\v{e}}p{\'a}nek, Zden{\v{e}}k {\v{Z}}abokrtsk{\'y}, Jan Haji{\v{c}}

We propose HamleDT â€• HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. While the license terms prevent us from directly redistributing the corpora, most of them are easily acquirable for research purposes. What we provide instead is the software that normalizes tree structures in the data obtained by the user from their original providers.

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Tasks

Dependency Parsing

HamleDT 2.0: Thirty Dependency Treebanks Stanfordized

2014-05-01 · LREC 2014 5 · Rudolf Rosa, Jan Ma{\v{s}}ek, David Mare{\v{c}}ek, Martin Popel 외

We present HamleDT 2.0 (HArmonized Multi-LanguagE Dependency Treebank). HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed int…

Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning

2017-09-19 · Kyungjae Lee, Sungjoon Choi, Songhwai Oh

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing

2026-02-03 · Yizhao Gao, Jianyu Wei, Qihao Zhang, Yu Cheng 외 arxiv

This work introduces Hybrid Sparse Attention (HySparse), a new architecture that interleaves each full attention layer with several sparse attention layers. While conceptually simple, HySparse strategically derives each …

Self-training a Constituency Parser using n-gram Trees

2014-05-01 · LREC 2014 5 · Arda {\c{C}}elebi, Arzucan {\"O}zg{\"u}r

In this study, we tackle the problem of self-training a feature-rich discriminative constituency parser. We approach the self-training problem with the assumption that while the full sentence parse tree produced by a par…

Constituency Parsing Sentence

Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine

2015-01-12 · Martin Sundin, Saikat Chatterjee, Magnus Jansson

Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existin…

Denoising Image Denoising