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As of cTAKES 2.5, NE Contexts is no longer a part of the default pipeline. Instead the newer assertion annotator is used to identify general attributes of EntityMentions (but not attributes specific to a single type of named entity such as the dosage attribute of a medication) |
The context annotator provides a mechanism for examining the context of existing annotations, finding events of interest in the context, and acting on those events in some way. The negation and status annotators both take advantage of this infrastructure by examining the context of named entities (e.g. disorders and findings) to see if they should be considered as negated (e.g. "no chest pain") or if their status should be modified (e.g. "myocardial infarction" should have status "history of").
In fact, the "negation annotator" is really just the context annotator configured to deal with negations. Similarly, the "status annotator" is the context annotator configured to identify the status of named entities.
To better understand the context annotator code you should start by reading the Javadocs for the class edu.mayo.bmi.uima.context.ContextAnnotator.java. It provides a conceptual overview of how the code works. |
The negation detection annotator is a pattern-based (no MaxEnt models required/used) approach that uses finite state machines and is roughly based on the popular NegEX algorithm introduced by Wendy Chapman (University of Pittsburgh)
What follows is an explanation of how negation is performed using the context annotator.
We will start by examining the descriptor file desc/NegationAnnotator.xml. It calls edu.mayo.bmi.uima.context.ContextAnnotator, instead of a "NegationAnnotator". In fact, there is no "negation annotator" analysis engine. We simply configure the ContextAnnotator for the task. Next we will discuss each of the parameter settings in turn:
So, the work of negating a named entity is done by:
The former is a pretty lightweight wrapper around another class which has all of the negation pattern finding logic, edu.mayo.bmi.fsm.machine.NegationFSM. If you want to update the pattern matching of negation detection, then you would do it in that class.
Updating the negation detection patterns will involve either:
The rules, patterns, words that identify negation are hard-coded into the class edu.mayo.bmi.fsm.machine.NegationFSM which is found in the core project. We would suggest starting off with the trial-and-error approach. For example, if you wanted to add "impossible" to the lexicon of negation words, then you could try adding it to the _negAdjectivesSet and test the behavior.
The way the status annotator works mirrors very closely how the negation annotator works. You are encouraged to read the above section, "Negation annotator", examine the parameter settings given for desc/StatusAnnotator.xml, and look at edu.mayo.bmi.fsm.machine.StatusIndicatorFSM.