Named Entity Recognition and Linking Augmented with Large-Scale Structured Data
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 27 Apr 2021.
Builds on
6 of 20 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
- 2017BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languagescited by 52
- 2020FLERT: Document-Level Features for Named Entity Recognitioncited by 45
Built on by
Not cited yet.