Evolving GATE to meet new challenges in language engineering

Bontcheva, Kalina; Tablan, Valentin; Maynard, Diana; Cunningham, Hamish

doi:10.1017/s1351324904003468

Cited by 130 publications

(80 citation statements)

References 27 publications

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“…The main aim of this identification would be to provide a NLP module with selected portions of text of interest, the hypothesis being that the reduction of background noise would allow for more effective extraction of the information. The ML facilities provided by the GATE framework [31] were used for all ML operations of this task: manual annotation, preliminary assessments, training and evaluation.…”

Section: Methodsmentioning

confidence: 99%

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Napolitano

Marshall

Hamilton

et al. 2016

Artificial Intelligence in Medicine

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Napolitano, Giulio et al., Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction, Artificial Intelligence in Medicine, 2016 Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction Giulio Napolitano Materials and methods:The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: 'semi-structured' and 'unstructured'. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry. Results:The best result of 99.4% accuracy -which included only one semi-structured report predicted as unstructured -was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured. Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

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Section: Methodsmentioning

confidence: 99%

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Napolitano

Marshall

Hamilton

et al. 2016

Artificial Intelligence in Medicine

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“…The records in free-text format were then processed using the GATE (General Architecture for Text Engineering natural language processing system [12][13][14].…”

Section: Processing Va Patient Recordsmentioning

confidence: 99%

Automatic Classification of Ultrasound Screening Examinations of the Abdominal Aorta

et al. 2016

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Our work facilitates the identification of veterans who may be at risk for abdominal aortic aneurysms (AAA) based on the 2007 mandate to screen all veteran patients that meet the screening criteria. The main research objective is to automatically index three clinical conditions: pertinent negative AAA, pertinent positive AAA, and visually unacceptable image exams. We developed and evaluated a ConText-based algorithm with the GATE (General Architecture for Text Engineering) development system to automatically classify 1402 ultrasound radiology reports for AAA screening. Using the results from JAPE (Java Annotation Pattern Engine) transducer rules, we developed a feature vector to classify the radiology reports with a decision table classifier. We found that ConText performed optimally on precision and recall for pertinent negative (0.99 (0.98-0.99), 0.99 (0.99-1.00)) and pertinent positive AAA detection (0.98 (0.95-1.00), 0.97 (0.92-1.00)), and respectably for determination of non-diagnostic image studies (0.85 (0.77-0.91), 0.96 (0.91-0.99)). In addition, our algorithm can determine the AAA size measurements for further characterization of abnormality. We developed and evaluated a regular expression based algorithm using GATE for determining the three contextual conditions: pertinent negative, pertinent positive, and non-diagnostic from radiology reports obtained for evaluating the presence or absence of abdominal aortic aneurysm. ConText performed very well at identifying the contextual features. Our study also discovered contextual trigger terms to detect sub-standard ultrasound image quality. Limitations of performance included unknown dictionary terms, complex sentences, and vague findings that were difficult to classify and properly code.

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“…General Architecture for text Engineering (GATE) [39][40][41] is another free and open software for both commercial and research purposes. It has been successfully used for Information Extraction purposes.…”

Section: Text Mining Expertisementioning

confidence: 99%