Comparison of Multilingual Entity Linking Approaches (2023)

Abstract: Despite rapid developments in the field of Natural Language Processing (NLP) in the past few years, the task of Multilingual Entity Linking (MEL) and especially its end-to-end formulation remains challenging. In this paper we aim to evaluate solutions for general end-to-end multilingual entity linking by conducting experiments using both existing complete approaches and novel combinations of pipelines for solving the task. The results identify the best performing current solutions and suggest some directions for further research.

## introduction entity linking (el) @xcite , @xcite is the task of mapping mentions in unstructured text to entities in an existing knowledge base (kb). it has drawn the attention of many researchers in the past few years due to its application in different areas of nlp, including question answering @xcite , @xcite , @xcite , relation extraction @xcite , dialogue @xcite , @xcite , @xcite and biomedical systems @xcite , @xcite . even though there has been a significant improvement in the field recently @xcite , @xcite , @xcite , the task of el and especially in the cross-lingual @xcite , @xcite , and mel setups remain challenging. different approaches have been proposed for solving this task, some of which are based on more traditional methods @xcite , @xcite and others exploit the recent discoveries in the field of natural language processing @xcite , @xcite , @xcite , @xcite . this paper will present experiments comparing the performance of various methods for a mel task. ## multilingual entity recognition and disambiguation methods in el, also known as named-entity recognition and disambiguation (nerd) words of interest in an unstructured text are mapped to corresponding unique entities in an existing target kb. formally it can be defined as the task of linking a given entity mention m in a given context c to the corresponding entity e in a kb. for the multilingual definition a set of languages l is added and the context is defined as language specific (context c of language l). it also requires a multilingual kb. as the name nerd suggests, the task consists of two subtasks, namely named-entity recognition (ner) and entity disambiguation (ed). two general groups of el methods exist. one focuses on performing entity disambiguation but requires correctly annotated entities or at least entity spans in its input. the second takes plain text input and performs both recognition and disambiguation in one or more steps. ner @xcite is a fundamental task in nlp which consists of recognising entities in text, and identifying their types. in the past years, different approaches have been developed, including statistical machine learning methods @xcite , @xcite , neural networks based ones @xcite , @xcite and a combination of both @xcite , @xcite . the recent advances in the field of nlp introduced the application of richer contextual embeddings computed via transformer models @xcite , @xcite , @xcite and have significantly improved the stateof-the-art (sota) of the task. in particular, these impressive results were achieved on benchmark datasets such as conll03 @xcite and ontonotes @xcite . nevertheless, it has been stated that the reason for this improvement lays not only on the model, but the fact that the benchmark datasets lack the presence of multiple practical challenges and these new models actually have problems detecting and classifying complex or unseen entities @xcite , @xcite . to address this issue, the dataset multiconer (malmasi et al., 2022) was developed which includes complex entity mentions with higher granularity on the type definition. with the introduction of the multiconer2 task @xcite , which focus on tackling multilingual named entity recognition (ner) in fine-grained and noisy scenarios, a lot of promising approaches for general entity recognition have been proposed @xcite , @xcite . ## evaluated approaches in our work we experiment with three different end-to-end el approaches: a classical approach, wikifier @xcite , and two systems that we build as a combination of mgenre @xcite with a multilingual ner model and a multilingual ebd method. ## discussion our comparison between wikifier and mgenre with respect to entity disambiguation shows that mgenre outperforms wikifier on mewsli-9. however, the linking accuracy of wikifier is comparable to one reported for mgenre and the difference comes from the er step. based on analysis of the ## conclusion and further work in this paper we attempted to explore and compare different end-to-end entity linking systems. apart from testing existing systems, we also build our own solutions as a combination of the state-of-theart entity disambiguation model mgenre with suitable named entity recognition or entity boundary detection methods. our results show that wikifier is capable of entity disambiguation which is slightly worse that the one achieved by mgenre. on the other hand its performance with respect to entity recognition is not satisfactory and requires significant improvement. another significant outcome of our work is that a combination of entity boundary detection method with mgenre and threshold filtering achieves the best overall performance on our custom dataset. in terms of entity disambiguation, mgenre demonstrates comparably high results to the ones reported, which is an indicator for its reliability. based on the separate results for entity (boundary) recognition and entity linking, we conclude that the performance of mgenre regarding correctly detected entities (boundaries) is quite satisfactory and can be applied in real world applications. for the improvement of the recall and precision of the end-to-end solution, improvements in the entity extraction is recommended. a possible future research direction in this field could be using large language models, llms @xcite for named entity recognition (as proposed in @xcite , @xcite ). apart from that, very recently, a transformer-based, end-to-end, one-pass multilingual system bela @xcite was released. a comparison of this system to the solutions explored in this work would also be valuable.