Abstract
We consider a joint information extraction(IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking. The used KB entity representations are learned from either(i) hyperlinked text documents (Wikipedia), or(ii) a knowledge graph (Wikidata), and ap-pear complementary in raising IE performance. Representations of corresponding entity linking (EL) candidates are added to text span representations of the input document, and we experiment with (i) taking a weighted average of the EL candidate representations based on their prior (in Wikipedia), and (ii) using an attention scheme over the EL candidate list. Results demonstrate an increase of up to 5%F1-score for the evaluated IE tasks on two datasets. Despite a strong performance of the prior-based model, our quantitative and qualitative analysis reveals the advantage of using the attention-based approach.
Highlights
Information extraction (IE) comprises several subtasks, e.g., named entity recognition (NER), coreference resolution, relation extraction (RE)
We address both research gaps of (a) integrating knowledge base (KB) information into a joint end-to-end IE model for solving named entity recognition, coreference resolution and relation extraction, and (b) analyzing what KB representation is more beneficial for IE, either KB-graph trained on Wikidata, or KB– text trained directly on Wikipedia
Deeper analysis reveals that adding KB representations mainly benefits performance for “rare” entity types: e.g., limiting the test set to entity types that occur ≤50 times in the training set for DWIE, compared to Baseline, F1 for NER goes up by +13.9 for KB-both with AttPrior, while the benefit gradually decreases for more frequently occurring entity types
Summary
Information extraction (IE) comprises several subtasks, e.g., named entity recognition (NER), coreference resolution (coref), relation extraction (RE). State-of-the-art results mainly report performance on single tasks, usually solving them on a sentence level (especially NER, RE). In practice, IE system decisions should be consistent on the document level, e.g., when processing news articles to automatically link entities (aside from potentially learning, e.g., new relations). It is well established that IE models benefit from incorporating background information of knowledge bases (KBs). Still, so far this has been shown from the perspective of solving individual tasks such as relation classification or entity typing (e.g., Peters et al (2019); Liu et al (2020)). Integrating KBs in joint models, realizing and analyzing the more complex end-to-end setting, has been left unexplored
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