19,109 research outputs found
Entity Recognition at First Sight: Improving NER with Eye Movement Information
Previous research shows that eye-tracking data contains information about the
lexical and syntactic properties of text, which can be used to improve natural
language processing models. In this work, we leverage eye movement features
from three corpora with recorded gaze information to augment a state-of-the-art
neural model for named entity recognition (NER) with gaze embeddings. These
corpora were manually annotated with named entity labels. Moreover, we show how
gaze features, generalized on word type level, eliminate the need for recorded
eye-tracking data at test time. The gaze-augmented models for NER using
token-level and type-level features outperform the baselines. We present the
benefits of eye-tracking features by evaluating the NER models on both
individual datasets as well as in cross-domain settings.Comment: Accepted at NAACL-HLT 201
Exploring galaxy evolution with generative models
Context. Generative models open up the possibility to interrogate scientific
data in a more data-driven way. Aims: We propose a method that uses generative
models to explore hypotheses in astrophysics and other areas. We use a neural
network to show how we can independently manipulate physical attributes by
encoding objects in latent space. Methods: By learning a latent space
representation of the data, we can use this network to forward model and
explore hypotheses in a data-driven way. We train a neural network to generate
artificial data to test hypotheses for the underlying physical processes.
Results: We demonstrate this process using a well-studied process in
astrophysics, the quenching of star formation in galaxies as they move from
low-to high-density environments. This approach can help explore astrophysical
and other phenomena in a way that is different from current methods based on
simulations and observations.Comment: Published in A&A. For code and further details, see
http://space.ml/proj/explor
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