# Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities

## TL;DR

This paper introduces a multi-level approach to representing entities using character, word, and entity embeddings, demonstrating that combining these levels enhances fine-grained entity typing accuracy.

## Contribution

It proposes a novel multi-level entity representation framework that integrates character, word, and entity embeddings, and shows this improves over existing methods.

## Key findings

- Joint multi-level representations outperform single-level baselines.
- Adding entity descriptions further enhances representation quality.
- Different learning methods excel at different representation levels.

## Abstract

Entities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, e.g., by neural networks), word (embeddings of words in entity names) and entity (entity embeddings). We investigate state-of-the-art learning methods on each level and find large differences, e.g., for deep learning models, traditional ngram features and the subword model of fasttext (Bojanowski et al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the entity level. We confirm experimentally that each level of representation contributes complementary information and a joint representation of all three levels improves the existing embedding based baseline for fine-grained entity typing by a large margin. Additionally, we show that adding information from entity descriptions further improves multi-level representations of entities.