[Paper Review] Effective Slot Filling Based on Shallow Distant Supervision Methods

Benjamin Roth, Tassilo Barth+3 more|arXiv (Cornell University)|Jan 6, 2014

Natural Language Processing Techniques8 references33 citations

TL;DR

This paper presents RelationFactory, an end-to-end relation extraction system that achieves state-of-the-art performance in the TAC KBP 2013 slot filling track using shallow distant supervision. By leveraging surface skip n-grams, optimized scoring for distant supervision patterns, and Wikipedia-based query expansion, the system attained an F1-score of 37.3%, significantly improving over its prior version with identical training data.

ABSTRACT

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%.

Motivation & Objective

• To improve slot filling performance in open-domain relation extraction using distant supervision.
• To develop a modular, end-to-end system that integrates document retrieval, relation prediction, and response post-processing.
• To enhance feature representation and scoring in distant supervision for better relation extraction accuracy.
• To explore query expansion via Wikipedia links to improve pattern recall and generalization.
• To achieve top performance in the TAC KBP 2013 English slot filling evaluation.

Proposed method

• The system employs shallow distant supervision to automatically generate training instances from knowledge bases and text corpora.
• Surface skip n-grams are used as the primary feature representation to capture local syntactic and semantic context around potential relations.
• A dedicated scoring mechanism is applied to rank and filter distant supervision patterns based on confidence and consistency.
• Query expansion is performed using Wikipedia links to enrich the query space and improve pattern recall.
• A training and tuning scheme is optimized for distant supervision classifiers to improve generalization and reduce noise.
• The pipeline integrates document retrieval, relation prediction, and post-processing in a modular architecture called RelationFactory.

Experimental results

Research questions

• RQ1 Can shallow distant supervision with surface skip n-grams improve slot filling performance in open-domain relation extraction?
• RQ2 How does query expansion via Wikipedia links affect the recall and precision of distant supervision patterns?
• RQ3 What is the impact of optimized scoring and feature representation on distant supervision classifier performance?
• RQ4 To what extent can a modular end-to-end system outperform prior systems using the same training data?
• RQ5 What factors contribute most significantly to improved F1-score in the TAC KBP 2013 slot filling evaluation?

Key findings

• The main run of the RelationFactory system achieved the highest F1-score of 37.3% in the TAC KBP 2013 English slot filling evaluation.
• The system significantly outperformed the previous year’s LSV system despite using the same training data and patterns.
• Feature representation based on surface skip n-grams contributed substantially to improved relation detection accuracy.
• Improved scoring of distant supervision patterns led to better filtering of noisy or incorrect relations.
• Query expansion using Wikipedia links enhanced the coverage and robustness of relation extraction patterns.
• The training and tuning scheme for distant supervision classifiers was a key factor in achieving high performance.