Extracting entities and relations for types of interest from text is
important for understanding massive text corpora. Traditionally, systems of
entity relation extraction have relied on human-annotated corpora for training
and adopted an incremental pipeline. Such systems require additional human
expertise to be ported to a new domain, and are vulnerable to errors cascading
down the pipeline. In this paper, we investigate joint extraction of typed
entities and relations with labeled data heuristically obtained from knowledge
bases (i.e., distant supervision). As our algorithm for type labeling via
distant supervision is context-agnostic, noisy training data poses unique
challenges for the task. We propose a novel domain-independent framework,
called CoType, that runs a data-driven text segmentation algorithm to extract
entity mentions, and jointly embeds entity mentions, relation mentions, text
features and type labels into two low-dimensional spaces (for entity and
relation mentions respectively), where, in each space, objects whose types are
close will also have similar representations. CoType, then using these learned
embeddings, estimates the types of test (unlinkable) mentions. We formulate a
joint optimization problem to learn embeddings from text corpora and knowledge
bases, adopting a novel partial-label loss function for noisy labeled data and
introducing an object "translation" function to capture the cross-constraints
of entities and relations on each other. Experiments on three public datasets
demonstrate the effectiveness of CoType across different domains (e.g., news,
biomedical), with an average of 25% improvement in F1 score compared to the
next best method.

本文介绍 “CoType”，一个数据驱动的文本分割算法和一种冠以知识库标签的实体及关系识别方法。实验表明，其在不同领域具有 25% 的 F1 得分提高。