Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

TL;DR

This paper introduces a novel embedding framework called PLE to reduce label noise in fine-grained entity typing by jointly embedding entity mentions, features, and types, leading to significant accuracy improvements.

Contribution

The paper proposes a new joint embedding method, PLE, that models semantic similarities and reduces label noise in entity typing tasks, outperforming previous approaches.

Findings

25% average accuracy improvement over previous methods

Effective noise robustness demonstrated on three datasets

Joint embedding captures semantic type relationships

Abstract

Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, the type labels so obtained from knowledge bases are often noisy (i.e., incorrect for the entity mention's local context). We define a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic identification of correct type labels (type-paths) for training examples, given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity between entity types pose unique challenges for solving the LNR task. We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type-path for each training example in a top-down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.