Named Entity Recognition and Linking Augmented with Large-Scale Structured Data
This work addresses the challenge of processing multilingual Web documents in highly inflected Slavic languages, providing a practical solution for NLP tasks in these languages.
The paper tackled Named Entity Recognition and Linking for Slavic languages by leveraging large-scale unstructured and structured data like Wikipedia and Wikidata, achieving effective entity recognition, normalization, and linking with minimal labeled training data.
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.