22 citations · 22 across the 2 of their papers we have counts for
4 papers
Towards Computationally Verifiable Semantic Grounding for Language Models
Chris Alberti, Kuzman Ganchev, Michael Collins +2
The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message for…
Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields
Georgi Georgiev, Preslav Nakov, Kuzman Ganchev +2
The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for…
State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Ji Ma, Kuzman Ganchev, David Weiss
A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined wi…
Globally Normalized Transition-Based Neural Networks
Daniel Andor, Chris Alberti, David Weiss +5
We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of-speech tagging, dependency parsing and sentence compression results.…