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Marker words for negation and speculation in health records and consumer reviews

Skeppstedt, Maria; Paradis, Carita LU and Kerren, Andreas (2016) 7th International Symposium on Semantic Mining in Biomedicine In CEUR Workshop Proceedings 1650. p.64-69
Abstract

Conditional random fields were trained to detect marker words for negation and speculation in two corpora belonging to two very different domains: clinical text and consumer review text. For the corpus of clinical text, marker words for speculation and negation were detected with results in line with previously reported interannotator agreement scores. This was also the case for speculation markers in the consumer review corpus, while detection of negation markers was unsuccessful in this genre. Also a setup in which models were trained on markers in consumer reviews, and applied on the clinical text genre, yielded low results. This shows that neither the trained models, nor the choice of appropriate machine learning algorithms and... (More)

Conditional random fields were trained to detect marker words for negation and speculation in two corpora belonging to two very different domains: clinical text and consumer review text. For the corpus of clinical text, marker words for speculation and negation were detected with results in line with previously reported interannotator agreement scores. This was also the case for speculation markers in the consumer review corpus, while detection of negation markers was unsuccessful in this genre. Also a setup in which models were trained on markers in consumer reviews, and applied on the clinical text genre, yielded low results. This shows that neither the trained models, nor the choice of appropriate machine learning algorithms and features, were transferable across the two text genres.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
CEUR Workshop Proceedings
volume
1650
pages
6 pages
conference name
7th International Symposium on Semantic Mining in Biomedicine
external identifiers
  • Scopus:84985912175
ISSN
1613-0073
language
English
LU publication?
yes
id
cb28dda6-de33-4a4e-bbb5-e69d78c3cd0a
alternative location
http://ceur-ws.org/Vol-1650/smbm2016Skeppstedt.pdf
date added to LUP
2016-09-30 09:03:50
date last changed
2016-09-30 13:46:08
@misc{cb28dda6-de33-4a4e-bbb5-e69d78c3cd0a,
  abstract     = {<p>Conditional random fields were trained to detect marker words for negation and speculation in two corpora belonging to two very different domains: clinical text and consumer review text. For the corpus of clinical text, marker words for speculation and negation were detected with results in line with previously reported interannotator agreement scores. This was also the case for speculation markers in the consumer review corpus, while detection of negation markers was unsuccessful in this genre. Also a setup in which models were trained on markers in consumer reviews, and applied on the clinical text genre, yielded low results. This shows that neither the trained models, nor the choice of appropriate machine learning algorithms and features, were transferable across the two text genres.</p>},
  author       = {Skeppstedt, Maria and Paradis, Carita and Kerren, Andreas},
  issn         = {1613-0073},
  language     = {eng},
  pages        = {64--69},
  series       = {CEUR Workshop Proceedings},
  title        = {Marker words for negation and speculation in health records and consumer reviews},
  volume       = {1650},
  year         = {2016},
}