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M. Willis Monroe

dblp:297/9744 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-9126-2991ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 100%
Theoretical computer science
1 paper
Automata and formal languages · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automata and formal languages
sequence modeling
0.612022
Sequence Models for Document Structure Identification in an Undeciphered Script · EMNLP 2022

Methods — techniques the papers use, named apart from their topics

unsupervised neural sequence modeling · 1.7statistical sequence modeling · 1.7
YearPublicationVenuePosition
2022 Sequence Models for Document Structure Identification in an Undeciphered Script
abstract
This work describes the first thorough analysis of "header" signs in proto-Elamite, an undeciphered script from 3100-2900 BCE.Headers are a category of signs which have been provisionally identified through painstaking manual analysis of this script by domain experts.We use unsupervised neural and statistical sequence modeling techniques to provide new and independent evidence for the existence of headers, without supervision from domain experts.Having affirmed the existence of headers as a legitimate structural feature, we next arrive at a richer understanding of their possible meaning and purpose by (i) examining which features predict their presence; (ii) identifying correlations between these features and other document properties; and (iii) examining cases where these features predict the presence of a header in texts where domain experts do not expect one (or vice versa).We provide more concrete processes for labeling headers in this corpus and a clearer justification for existing intuitions about document structure in proto-Elamite.
Logan Born, M. Willis Monroe, Kathryn Kelley, Anoop Sarkar
EMNLP2