Multilingual S lavic Named Entity Recognition (2021)

Abstract: Named entity recognition, in particular for morphological rich languages, is challenging task due to the richness of inflected forms and ambiguity. This challenge is being addressed by SlavNER Shared Task. In this paper we describe system submitted to this task. Our system uses pre-trained multilingual BERT Language Model and is fine-tuned for six Slavic languages of this task on texts distributed by organizers. In our experiments this multilingual NER model achieved 96 F1 score on in-domain data and an F1 score of 83 on out of domain data. Entity coreference module achieved F1 score of 47.6 as evaluated by bsnlp2021 organizers.

## introduction named entity recognition, in particular for morphological rich languages, is challenging task due to the richness of inflected forms and their ambiguity. evaluation results are usually lower for these languages, when compared to morphologically simpler languages. for instance, for finnish language @xcite reports the f1 score of 92.4 on in-domain data and 81.47 on out of domain data, for latvian state-of-the-art ner system @xcite achieves the f1 score 82.6, while for english luke model @xcite achieves f1 score of 94.3 on conll-2003 dataset. in this paper we present our submission to the slavner shared task on the analysis of named entities in multilingual web documents in slavic languages. our submission implements modular architecture, consisting of three modules that correspond to the tasks of the shared task -named entity recognition, entity normalization and multilingual entity linking. results of the previous challenges on named entity recognition show that fine-tuning of a large language model leads to the best overall result. using this approach in previous shared task the best result was achieved by @xcite , allowing to reach 87.2 f1 score for bulgarian, 87.3 f1 for russian, 93.2 f1 for polish and 93.9 f1 score for czech. for named entity linking task we use a dynamic knowledge base, which is built at run-time using identified entity mentions and their embeddings, similar to @xcite . our model uses pre-trained labse model @xcite to obtain aligned embeddings in different languages. we achieve average f1 score of 48.8 (us election 2020 dataset f1 score 51.98 and covid-19 dataset f1 score 42.3). ## data preparation we use data provided by shared task organizers to train the named entity recognition (ner) component. the data consists of raw text documents with some metadata (language, source, title, date, file-id) and annotation documents. each annotation file contains a file-id linking it to the respective text document and list of named entities present in document. in order to train ner, we transformed data into conll2003 like format. at first, raw documents were split into sentences using nltk library @xcite . language specific nltk models were used for sentence segmentation where they were available and russian model was applied when language specific models were not available. each token in sentence is labeled as either belonging to one of the named entity classes used in this task or labeled with label "o". although in this dataset documents are categorized into 5 topics -"asia-bibi", "brexit", "nord-stream", "ryanair" and "other", we train single model for all topics. as an additional data for training, we explored various data sets in languages covered by bsnlp2021, however, in our opinion none of publicly available data sets match entity types required in this task. we found a data set in latvian (znotin ¸š, 2015), which includes the same entities as this task (person, location, organization, event and product). since latvian is also a morphologically rich inflected language, we decided to train a ner system using this data in addition to the data provided by shared task organizers. ## architecture and modules the architecture of our system is modular, consisting of three modules (figure 1 ). at first, ner component identifies candidate entity mentions. then, entity mentions are linked to already found entity mentions or added as new entity to a list. finally, a base form for a given entity mention is obtained. ## conclusion in this paper we proposed modular architecture that allows to find named entities in six slavic languages and links identified entities to the same entity in other documents in different languages. each module can be separately updated to improve system performance. as the next step, constituent modules of this system could be improved, for example with entity normalization rules for multi-word entities or implementing longer context for obtaining entity embeddings for linking.