Making large-scale support vector machine learning practical
Advances in kernel methods
Machine Learning
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
A First Step Towards the Integration of Accident Reports and Constructive Design Documents
SAFECOMP '99 Proceedings of the 18th International Conference on Computer Computer Safety, Reliability and Security
An Experiment with Browsers that Learn
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Sequence modelling for sentence classification in a legal summarisation system
Proceedings of the 2005 ACM symposium on Applied computing
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automated text summarization and the SUMMARIST system
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
An Application of Semantic Annotations to Design Errors
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
A bootstrapping approach to unsupervised detection of cue phrase variants
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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Human Errors, e.g. a pilot mismanaged the fuel system causing engine failure and fuel starvation, are known to contribute to over 66% of aviation accidents. However, in some cases, the real sources of the errors are the design of aircraft, e.g.the pilot was confused with the different fuel systems across different models in the same manufacture. The failed collaboration between human operators and the systems therefore has been a major concern for aviation industries. Aviation accident reports are critical information sources to understand how to prevent or reduce such problematic collaboration. In particular, the portions of the reports describing how the behaviour of human operators deviated from an established norm and how the design of aircraft systems contributed to this deviation are particularly important. However, it is a time-consuming and error-prone task to manually extract such information from the reports. One reason is that current accident reports do not aim specifically at capturing the information in format easily accessible for aircraft designers. Therefore, an automatic approach that identifies the sentences describing Human Errors and Design Errors is needed. A preliminary test using hand-crafted cue phrases, i.e. a special word or phrases that are used to indicate the types of sentences, showed a limited identification performance. Therefore, a machine learning technique that uses a greater variety of the linguistic features of the cue phrases than the pre-defined ones and makes the identification decisions based on the combinations these features, looks promising. The examples of the features are active or passive sentence styles and the position of keywords in the sentence. This paper presents the results of developing such automastic identification approach.