Effective Slot Filling Based on Shallow Distant Supervision Methods
This work addresses slot filling for spoken language systems, representing an incremental improvement over previous methods.
The paper tackled the problem of slot filling in relation extraction by developing a modular end-to-end system called RelationFactory, which achieved a top-ranked F1-score of 37.3% in the TAC KBP 2013 English Slotfilling evaluation through improvements in feature representation and scoring methods.
Spoken Language Systems at Saarland University (LSV) participated this year with 5 runs at the TAC KBP English slot filling track. Effective algorithms for all parts of the pipeline, from document retrieval to relation prediction and response post-processing, are bundled in a modular end-to-end relation extraction system called RelationFactory. The main run solely focuses on shallow techniques and achieved significant improvements over LSV's last year's system, while using the same training data and patterns. Improvements mainly have been obtained by a feature representation focusing on surface skip n-grams and improved scoring for extracted distant supervision patterns. Important factors for effective extraction are the training and tuning scheme for distant supervision classifiers, and the query expansion by a translation model based on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the submitted main run of the LSV RelationFactory system achieved the top-ranked F1-score of 37.3%.