activity
20162024
most citedFeature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields

22 citations · 23 across the 4 of their papers we have counts for

collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2024★ 1 cited

DOLOMITES: Domain-Specific Long-Form Methodical Tasks

Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev +7

Experts in various fields routinely perform methodical writing tasks to plan, organize, and report their work. From a clinician writing a differential diagnosis for a patient, to a…

cs.CL2023

Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation

Fantine Huot, Joshua Maynez, Shashi Narayan +6

While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelev…

cs.CL2022

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…

cs.CL2021★ 22 cited

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…

cs.CL2018

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…

cs.CL2016

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.…