526 citations · 555 across the 4 of their papers we have counts for
7 papers
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem, Abhishek Shah, Hui Wan +3
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score…
Arc-Standard Spinal Parsing with Stack-LSTMs
Miguel Ballesteros, Xavier Carreras
We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees. The parser uses Stack-LSTMs that compose constituent nodes with depe…
AMR Parsing using Stack-LSTMs
Miguel Ballesteros, Yaser Al-Onaizan
We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our ex…
Are Emojis Predictable?
Francesco Barbieri, Miguel Ballesteros, Horacio Saggion
Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message. Despite being widely used in social media, their unde…
Neural Architectures for Named Entity Recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian +2
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised train…
Static and Dynamic Feature Selection in Morphosyntactic Analyzers
Bernd Bohnet, Miguel Ballesteros, Ryan McDonald +1
We study the use of greedy feature selection methods for morphosyntactic tagging under a number of different conditions. We compare a static ordering of features to a dynamic order…