Empower Entity Set Expansion via Language Model Probing (2020)

Abstract: Entity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and IR applications, such as question answering, query understanding, and taxonomy construction. Existing set expansion methods bootstrap the seed entity set by adaptively selecting context features and extracting new entities. A key challenge for entity set expansion is to avoid selecting ambiguous context features which will shift the class semantics and lead to accumulative errors in later iterations. In this study, we propose a novel iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. In each iteration, we select one positive and several negative class names by probing a pre-trained language model, and further score each candidate entity based on selected class names. Experiments on two datasets show that our framework generates high-quality class names and outperforms previous state-of-the-art methods significantly.

## introduction entity set expansion aims to expand a small set of seed entities (e.g., {"united states", "china", "canada"}) with new entities (e.g., "united kingdom", "australia") belonging to the same semantic class (i.e., country). the entities so discovered may benefit a variety of nlp and ir applications, such as question answering @xcite , query understanding @xcite , taxonomy construction @xcite , and semantic search @xcite . most existing entity set expansion methods bootstrap the initial seed set by iteratively selecting context features (e.g., co-occurrence words @xcite , unary patterns @xcite , and coordinational patterns @xcite ), ## background in this section, we provide background on language models and define the entity set expansion problem. ## class-guided entity set expansion we introduce our class-guided entity set expansion framework in this section. first, we present our class name generation and ranking modules in sections 3.1 and 3.2, respectively. then, we discuss how to leverage class names to guide the iterative expansion process in section 3.3. ## conclusions in this paper, we propose a new entity set expansion framework that can use a pre-trained lm to generate candidate class names for the seed set, rank them according to the provided text corpus, and guide the entity selection process with the selected class names. extensive experiments on the wiki and apr datasets demonstrate the effectiveness of our framework on both class name prediction and entity set expansion. in the future, we plan to expand the method scope from expanding concrete entity sets to more abstract concept sets. for example, we may expand the set {"machine translation", "information extraction", "syntactic parsing"} to acquire more nlp task concepts. another interesting direction is to generate a class name hierarchy via language model probing.