Evidence map›Paper›PMID 34194119›Full record

ArticleJournal. Washington Academy of Sciences, Washington, D. C2018

Generating Domain Terminologies using Root- and Rule-Based Terms.

Jacob Collard, T N Bhat, Eswaran Subrahmanian, Ram D Sriram, John T Elliot, Ursula R Kattner, Carelyn E Campbell, Ira Monarch

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Journal. Washington Academy of Sciences, Washington, D. C, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jacob CollardIndependent Consultant, Ithaca, New York.
T N BhatMaterials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD.
Eswaran SubrahmanianInformation Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD.
Ram D SriramInformation Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD.
John T ElliotMaterials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD.
Ursula R KattnerMaterials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD.
Carelyn E CampbellMaterials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD.
Ira MonarchCarnegie Mellon University, Pittsburgh, PA.

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

Abstract

Motivated by the need for flexible, intuitive, reusable, and normalized terminology for guiding search and building ontologies, we present a general approach for generating sets of such terminologies from natural language documents. The terms that this approach generates are root- and rule-based terms, generated by a series of rules designed to be flexible, to evolve, and, perhaps most important, to protect against ambiguity and standardize semantically similar but syntactically distinct phrases to a normal form. This approach combines several linguistic and computational methods that can be automated with the help of training sets to quickly and consistently extract normalized terms. We discuss how this can be extended as natural language technologies improve and how the strategy applies to common use-cases such as search, document entry and archiving, and identifying, tracking, and predicting scientific and technological trends.

Indexed as

dependency parsingnatural language processingontology generationsearchterminology generationunsupervised learning

Identifiers

PMID34194119
PMCPMC8240749

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.