Named Entity Recognition and Linking Augmented with Large-Scale Structured Data (2021)

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.

## introduction intelligent analysis of texts written in natural languages, despite the advancements made with deep neural networks, is still regarded as challenging. the lingua franca of science is english, and new methods are typically evaluated firstly on english data, and often on other germanic or romance languages. this puts a certain bias on the development and design of modern nlp methods, which are not always transferable, and the metrics comparable, across languages and language families. due to the complexity and inherent vagueness of intelligent language processing, is has been naturally split into simple tasks, one of which is named entity recognition (ner), concerned in this paper. the output of a ner system is traditionally a set labelled phrases recognized in a given text. in order to process a document, one has to not only find and label the entities, but also link appropriately subsequent occurrences of the same entity. the task becomes harder, if the linking can be made across languages, when the entities are globally present. we describe our submission to the 3rd multilingual named entity challenge in slavic languages, held at the 8th workshop on balto-slavic natural language processing (bsnlp) in conjunction with the eacl 2021 conference. the system was similar to the one submitted to the 2nd multilingual named entity challenges in slavic languages @xcite held at 7th bsnlp workshop in conjunction with acl 2019 conference, and we discuss the differences between both systems. the aim of those shared tasks was to recognize, normalize, and ultimately link -on a document, language and cross-language level -all named entities in collections of documents concerning the same topic, e.g., the 2020 us presidential election. named entities have been split into five categories: per (person), loc (location), org (organization), pro (product), and evt (event). the 2019 edition featured four slavic languages (czech, russian, bulgarian, polish), and the 2021 edition featured six languages (the previous four plus ukrainian and sloven). in our solution we have combined models trained unsupervised on large datasets, and fine-tuned on small ones in a supervised way, with simple, whitebox algorithms that perform later stages of processing in a stable and predictable manner. in addition, we have taken advantage of similarities between certain languages in order to augment the data and further improve the results. ## our approach our system chains three modules for named entity recognition, lemmatization, and linking, which correspond to the objectives of the bsnlp shared task. we describe them in detail in the following sections. our submissions for the 2019 and the 2021 shared tasks were similar, and differed ## evaluation we present experiments carried out on different levels of the entity recognition pipeline. the data used in those experiments comes from the bsnlp 2019 shared task test set (nord stream and ryanair subsets). our algorithms are tested in the submitted form and have not been further adapted to those datasets. ## conclusion this paper describes our submissions to the 2019 and 2021 bsnlp shared tasks on named entity recognition on slavic languages. even though the training data was scarce, we have used large-scale datasets: corpora of unstructured text in the unsupervised training phase of training of the recognition model, and structured wikipedia and wikidata knowledge bases in order to extract rules and entities for lemmatization and linking phases. the linking algorithm is a strong point of our submission. in the 2019 task it allowed to close the performance gap between our solution and competitors, introduced by a weak initial recognition model. the results suggest that, perhaps, there is still a white spot in between supervised and unsupervised neural learning, where the structure of the data matters more than volume, and simple rule-based system excel.