Gildea and Jurafsky Computational Linguistics 2002
Citation
Daniel Gildea and Daniel Jurafsky. 2002. Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3):245-288.
Online version
Summary
The paper presents a system for Semantic Role Labeling.
They divided their extraction job into three tasks below.
- Extraction of medical terms
- Relation Extraction
- In this paper, relation extraction means extraction of associated medical concepts. For example, 'Blood pressure' and '144/90' are associated terms in the sentence, "Blood pressure is 144/90".
- Text Classification
- For example, a patient can be classified as a former smoker, a current smoker, or a non-smoker
Their approaches are:
- An ontology-based approach for extracting medical terms of interest
- They used Unified Medical Language System (UMLS). About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures.
- A graph-based approach which uses the parsing result of link-grammar parser for Relation Extraction
- Notable things in their approach are three. First, they included the processing of negation. Second, when the parser fails, they used a pattern-based approach. Lastly, they replaced multi-word terms with placeholders because the parser did not process the terms.
- an NLP-based feature extraction method coupled with an ID3-based Decision Tree Learning for Text Classification
This approach was fairly successful mostly showing over 80% of precision and recall. However, the system was tested on the data written by only a clinician, which means that the style of free-text records was consistent. Nevertheless, the research is worth in that they applied various IE techniques to the free-text clinical records, explain about the problems they encountered.
Related papers
An interesting follow-up paper is Denecke and Bernauer AIME 2007 which uses semantic structures to extract medical information.
