Semantic Entity Retrieval Toolkit

TL;DR

The paper introduces SERT, a toolkit for learning and applying semantic representations of words and entities, supporting various algorithms, GPU use, and easy customization for downstream tasks.

Contribution

SERT provides a unified, flexible framework for entity representation learning, enabling efficient training, ranking, and extraction for diverse NLP applications.

Findings

Supports multiple representation learning algorithms

Enables GPU-accelerated training and inference

Facilitates downstream clustering and recommendation tasks

Abstract

Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention. In this paper we describe the Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our previously published entity representation models. The toolkit provides a unified interface to different representation learning algorithms, fine-grained parsing configuration and can be used transparently with GPUs. In addition, users can easily modify existing models or implement their own models in the framework. After model training, SERT can be used to rank entities according to a textual query and extract the learned entity/word representation for use in downstream algorithms, such as clustering or recommendation.