An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing (2021)

Abstract: Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET). However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET. In this paper, we empirically study three kinds of auxiliary information: context consistency, type hierarchy and background knowledge (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources. The performance obtains up to 11.42% and 22.84% absolute gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores. More importantly, we further discuss the characteristics, merits and demerits of each information source and provide an intuitive understanding of the complementarity among them.

## introduction fine-grained entity typing (fet) aims to detect the types of an entity mention given its context @xcite @xcite . the results of fet benefit lots of downstream tasks @xcite @xcite @xcite . in many scenarios, the type hierarchy is continuously evolving, which requires newly emerged types to be accounted into fet systems. as a result, zero-shot fet (zfet) is welcomed to handle the new types which are unseen during training stage @xcite @xcite . the major challenge of zfet is to build the semantic connections between the seen types (during training) and the unseen ones (during inference). ## related work as a zero-shot paradigm of fet, zfet suffers from a huge information gap between the seen and unseen types due to the lack of annotated data. in spite of simply computing type representations by averaging the embeddings of words comprising their names @xcite , a variety of auxiliary information has been explored to fill this gap. huang et al. (2016) proposes a hierarchical clustering model with domain-specific knowledge base for unsupervised entity typing. ma et al. (2016) first introduces prototypical information to learn type embeddings and encodes type hierarchy by multiplying the type embeddings with a binary hierarchical matrix. zhou et al. (2018) matches the entity mention with a set of wikipedia entries and classifies the mention based on the freebase types of its type-compatible entries. obeidat et al. (2019) leverages wikipedia descriptions of types and designs a context-description matching model. ren et al. (2020) employs entity type attention to make the model focus on context semantically relevant to the type. zhang et al. (2020b) transfers the knowledge from seen types to the unseen ones through memory network. as for context consistency, xin et al. (2018) first takes the language models as constraint in supervised typing tasks. recently, qian et al. (2021) studies unsupervised entity typing without using knowledge base, where pseudo data with fine-grained labels are automatically created from large unlabeled dataset. ## conclusion in this paper, we explored multiple information sources for zfet. we proposed a multi-source fusion model to better integrate these sources, which has achieved state-of-the-art performance in zfet. besides, we conducted a deep analysis about the characteristics, merits and demerits of each information source, and discussed the complementarity among different sources. in particular, the context consistency information from the pre-trained language model is relatively useful in complex scenarios with long-tail types or long contexts. along this way, we will conduct more in-depth research to take full advantage of context consistency. besides, we will also explore more reasonable methods for information fusion in zfet.