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Found 10 papers, 0 papers with code
Observational Comparison of Geo-tagged and Randomly-drawn Tweets
Twitter is a ubiquitous source of micro-blog social media data, providing the academic, industrial, and public sectors real-time access to actionable information.
Portable, layer-wise task performance monitoring for NLP models
There is a long-standing interest in understanding the internal behavior of neural networks.
CADET: Computer Assisted Discovery Extraction and Translation
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
Graph convolutional networks for exploring authorship hypotheses
This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources.
JHU System Description for the MADAR Arabic Dialect Identification Shared Task
Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models.
Graph-Convolutional Autoencoder Ensembles for the Humanities, Illustrated with a Study of the American Slave Trade
We introduce a graph-aware autoencoder ensemble framework, with associated formalisms and tooling, designed to facilitate deep learning for scholarship in the humanities.
Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses
We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin.
Detecting Structured Language Alternations in Historical Documents by Combining Language Identification with Fourier Analysis
In this study, we present a generalizable workflow to identify documents in a historic language with a nonstandard language and script combination, Armeno-Turkish.
Pairing Orthographically Variant Literary Words to Standard Equivalents Using Neural Edit Distance Models
We present a novel corpus consisting of orthographically variant words found in works of 19th century U. S. literature annotated with their corresponding "standard" word pair.
