Hybrid Ensemble-Rule Algorithm for Improved MEDLINE® Sentence Boundary Detection.

Daniel X Le, James G Mork, Sameer Antani

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Daniel X LeLister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894.

James G MorkLister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894.

Sameer AntaniLister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sentence boundary detection (SBD) is a fundamental building block in the Natural Language Processing (NLP) pipeline. Incorrect SBD may impact subsequent processing stages resulting in decreased performance. In well-behaved corpora, a few simple rules based on punctuation and capitalization are sufficient for successfully detecting sentence boundaries. However, a corpus like MEDLINE citations presents challenges for SBD due to several syntactic ambiguities, e.g., abbreviation-periods, capital letters in first words of sentences, etc. In this manuscript we present an algorithm to address these challenges based on majority voting among three SBD engines (Python NLTK, pySBD, and Syntok) followed by custom post-processing algorithms that rely on NLP spaCy part-of-speech, abbreviation and capital letter detection, and computing general sentence statistics. Experiments on several thousand MEDLINE citations show that our proposed approach for combining multiple SBD engines and post-processing rules performs better than each individual engine.

Indexed as

LanguageNatural Language ProcessingAlgorithmsHumansMEDLINE

Identifiers

PMID35308957

PMCPMC8861722

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.