Comparative evaluation of boundary-relaxed annotation for Entity Linking performance
Gabriel Herman Bernardim Andrade, Shuntaro Yada, Eiji Aramaki
摘要
Entity Linking performance has a strong reliance on having a large quantity of high-quality annotated training data available. Yet, manual annotation of named entities, especially their boundaries, is ambiguous, error-prone, and raises many inconsistencies between annotators. While imprecise boundary annotation can degrade a model's performance, there are applications where accurate extraction of entities' surface form is not necessary. For those cases, a lenient annotation guideline could relieve the annotators' workload and speed up the process. This paper presents a case study designed to verify the feasibility of such annotation process and evaluate the impact of boundary-relaxed annotation in an Entity Linking pipeline. We first generate a set of noisy versions of the widely used AIDA CoNLL-YAGO dataset by expanding the boundaries subsets of annotated entity mentions and then train three Entity Linking models on this data and evaluate the relative impact of imprecise annotation on entity recognition and disambiguation performances. We demonstrate that the magnitude of effects caused by noise in the Named Entity Recognition phase is dependent on both model complexity and noise ratio, while Entity Disambiguation components are susceptible to entity boundary imprecision due to strong vocabulary dependency. Original annotation Noisy annotation CRICKET -LEICESTERSHIRE TAKE OVER AT TOP AFTER INNINGS VICTORY. LONDON 1996-08-30 West Indian all-rounder Phil Simmons took four for 38 on Friday as Leicestershire beat Somerset by an innings and 39 runs in two days to take over at the head of the county championship. CRICKET -LEICESTERSHIRE TAKE OVER AT TOP AFTER INNINGS VICTORY. LONDON 1996-08-30 West Indian all-rounder Phil Simmons took four for 38 on Friday as Leicestershire beat Somerset by an innings and 39 runs in two days to take over at the head of the county championship.
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- EntQA: Entity Linking as Question AnsweringWenzheng Zhang, Wenyue Hua, Karl StratosICLR 2022 · 被引用 64 次
- Weakly Supervised Named Entity Tagging with Learnable Logical RulesJiacheng Li, Haibo Ding, Jingbo Shang, Julian J. McAuley 等ACL 2021
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