Named Entity Recognition and Linking Augmented with Large-Scale Structured Data

TL;DR

This paper presents a system for multilingual Named Entity Recognition and Linking that leverages large-scale structured and unstructured data, including Wikipedia and Wikidata, to improve recognition and linking with minimal labeled data.

Contribution

The authors introduce a novel approach combining structured data from Wikidata and unstructured data for NER and linking in Slavic languages, requiring only small labeled datasets.

Findings

Effective recognition and linking in Slavic languages

Utilization of Wikipedia and Wikidata enhances performance

Minimal labeled data needed for training

Abstract

In this paper we describe our submissions to the 2nd and 3rd SlavNER Shared Tasks held at BSNLP 2019 and BSNLP 2021, respectively. The tasks focused on the analysis of Named Entities in multilingual Web documents in Slavic languages with rich inflection. Our solution takes advantage of large collections of both unstructured and structured documents. The former serve as data for unsupervised training of language models and embeddings of lexical units. The latter refers to Wikipedia and its structured counterpart - Wikidata, our source of lemmatization rules, and real-world entities. With the aid of those resources, our system could recognize, normalize and link entities, while being trained with only small amounts of labeled data.