Automatic discourse connective detection in biomedical text.
J Am Med Inform Assoc
; 19(5): 800-8, 2012.
Article
en En
| MEDLINE
| ID: mdl-22744958
ABSTRACT
OBJECTIVE:
Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. MATERIALS ANDMETHODS:
Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (~112,000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (~1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores. RESULTS ANDCONCLUSION:
Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at http//bioconn.askhermes.org.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
Minería de Datos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Am Med Inform Assoc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Estados Unidos