arXiv subjects

Brian Lester

Publications and source records attributed to Brian Lester.

21 records · Page 2Linked to original sources

arXiv2020

iobes: A Library for Span-Level Processing

Many tasks in natural language processing, such as named entity recognition and slot-filling, involve identifying and labeling specific spans of text. In order to leverage common models, these tasks are often recast as sequence labeling tasks. Each token is given a label and these labels are prefixed with special tokens such as B- or I-. After a model assigns labels to each token, these prefixes are used to group the tokens into spans. Properly parsing these annotations is critical for producing fair and comparable metrics; however, despite its importance, there is not an easy-to-use, standardized, programmatically integratable library to help work with span labeling. To remedy this, we introduce our open-source library, iobes. iobes is used for parsing, converting, and processing spans represented as token level decisions.

cs.CL↗

arXiv2019

An Effective Label Noise Model for DNN Text Classification

Because large, human-annotated datasets suffer from labeling errors, it is crucial to be able to train deep neural networks in the presence of label noise. While training image classification models with label noise have received much attention, training text classification models have not. In this paper, we propose an approach to training deep networks that is robust to label noise. This approach introduces a non-linear processing layer (noise model) that models the statistics of the label noise into a convolutional neural network (CNN) architecture. The noise model and the CNN weights are learned jointly from noisy training data, which prevents the model from overfitting to erroneous labels. Through extensive experiments on several text classification datasets, we show that this approach enables the CNN to learn better sentence representations and is robust even to extreme label noise. We find that proper initialization and regularization of this noise model is critical. Further, by contrast to results focusing on large batch sizes for mitigating label noise for image classification, we find that altering the batch size does not have much effect on classification performance.

cs.LG↗

arXiv2011

Search for plant biomagnetism with a sensitive atomic magnetometer

We report what we believe is the first experimental limit placed on plant biomagnetism. Measurements with a sensitive atomic magnetometer were performed on the Titan arum (Amorphophallus titanum) inflorescence, known for its fast bio-chemical processes while blooming. We find that the surface magnetic field from these processes, projected along the Earth's magnetic field, and measured at the surface of the plant, is less then ~0.6uG.

physics.atom-ph↗