University of Cambridge > Talks.cam > NLIP Seminar Series > Combining Manual Rules and Supervised Learning for Hedge Cue and Scope Detection

Combining Manual Rules and Supervised Learning for Hedge Cue and Scope Detection

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Hedge cues were detected using a supervised Conditional Random Field (CRF) classifier exploiting features from the RASP parser. The CRF ’s predictions were filtered using known cues and unseen instances were removed, increasing precision while retaining recall. Rules for scope detection, based on the grammatical relations of the sentence and the part-of-speech tag of the cue, were manually developed. However, another supervised CRF classifier was used to refine these predictions. As a final step, scopes were constructed from the classifier output using a small set of post-processing rules. Development of the system revealed a number of issues with the annotation scheme adopted by the organisers.

Joint work with Ted Briscoe.

This talk is part of the NLIP Seminar Series series.

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