Limits...
A rule based solution to co-reference resolution in clinical text.

Chen P, Hinote D, Chen G - J Am Med Inform Assoc (2012)

Bottom Line: Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains.Normally, there are two ways of constructing a system to automatically discoverco-referent links.Our system achieved 89.6% overall performance on multiple medical datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Mathematical Sciences, University of Houston-Downtown, Houston, Texas, USA.

ABSTRACT

Objective: To build an effective co-reference resolution system tailored to the biomedical domain.

Methods: Experimental materials used in this study were provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves co-reference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains. Normally, there are two ways of constructing a system to automatically discoverco-referent links. One is to manually build rules forco-reference resolution; the other is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets.

Results: The existing co-reference resolution systems are able to find some of the co-referent links; our rule based system performs well, finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets.

Conclusions: Manually crafted rules based on observation of training data is a valid way to accomplish high performance in this co-reference resolution task for the critical biomedical domain.

Show MeSH

Related in: MedlinePlus

Representation of document handler functionality. Access the article online to view this figure in colour.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3756251&req=5

AMIAJNL20121000770F2: Representation of document handler functionality. Access the article online to view this figure in colour.

Mentions: The first two routines in the system are made to read in the text being examined and the concepts that are to be linked from the files provided by i2b2. The document handler breaks the text into tokens using white space boundaries, with each space character indicating the end of one word and the beginning of the next. The text is then stored in a two-dimensional array, where the first dimension is the line number, and the second dimension is the word number. A representation of this operation is depicted in figure 2.


A rule based solution to co-reference resolution in clinical text.

Chen P, Hinote D, Chen G - J Am Med Inform Assoc (2012)

Representation of document handler functionality. Access the article online to view this figure in colour.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756251&req=5

AMIAJNL20121000770F2: Representation of document handler functionality. Access the article online to view this figure in colour.
Mentions: The first two routines in the system are made to read in the text being examined and the concepts that are to be linked from the files provided by i2b2. The document handler breaks the text into tokens using white space boundaries, with each space character indicating the end of one word and the beginning of the next. The text is then stored in a two-dimensional array, where the first dimension is the line number, and the second dimension is the word number. A representation of this operation is depicted in figure 2.

Bottom Line: Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains.Normally, there are two ways of constructing a system to automatically discoverco-referent links.Our system achieved 89.6% overall performance on multiple medical datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Mathematical Sciences, University of Houston-Downtown, Houston, Texas, USA.

ABSTRACT

Objective: To build an effective co-reference resolution system tailored to the biomedical domain.

Methods: Experimental materials used in this study were provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves co-reference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are linked by co-reference chains. Normally, there are two ways of constructing a system to automatically discoverco-referent links. One is to manually build rules forco-reference resolution; the other is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets.

Results: The existing co-reference resolution systems are able to find some of the co-referent links; our rule based system performs well, finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets.

Conclusions: Manually crafted rules based on observation of training data is a valid way to accomplish high performance in this co-reference resolution task for the critical biomedical domain.

Show MeSH
Related in: MedlinePlus