L 3 I ++ at S em E val-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition (2023)

Abstract: This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2023 Task 2, Multilingual Complex Named Entity Recognition (MultiCoNER II). Similar to MultiCoNER I, the task seeks to develop methods to detect semantic ambiguous and complex entities in short and low-context settings. However, MultiCoNER II adds a fine-grained entity taxonomy with over 30 entity types and corrupted data on the test partitions. We approach these complications following prompt-based learning as (1) a ranking problem using a seq2seq framework, and (2) an extractive question-answering task. Our findings show that even if prompting techniques have a similar recall to fine-tuned hierarchical language model-based encoder methods, precision tends to be more affected.

## introduction in semeval-2022, multilingual complex named entity recognition (multiconer) i @xcite (task 11) aimed at developing complex ner systems for 11 languages. the task focused on detecting semantically ambiguous and complex entities in short, lowercase, low-context monolingual, and multilingual settings. these challenges needed to be approached, while testing the domain and language adaption capability of the ner systems proposed by the participants. thus, our participation consisted of a robust approach for tackling the following challenges for the language-specific, multilingual, and mixed sub-tasks @xcite : (1) short texts: we built multilingual contexts with and without entities, in order to improve the focus of the ner system more on the context and less on the other surrounding entities; (2) lowercase texts: we prioritized the usage of uncased language models; and (3) code-mixed and low-resource languages: we added a multilingual language model along with a language-specific language model. our findings showed that, while adding contexts (from train partitions), with and without entities, was promising, the topics or domains overlap influenced the performance in both directions. in semeval-2023, multiconer ii @xcite ) (task 2) featured complex ner in these languages (slight changes in comparison with the previous multiconer): english, spanish, hindi, bangla, chinese, swedish, farsi, french, italian, portuguese, ukrainian, and german. in addition to multilingualism, there were two new key challenges: (1) a fine-grained entity taxonomy with over 30 different classes, and (2) simulated errors added to the test set to make the task more realistic and difficult. approaching these challenges would require an ability to easily adapt to the taxonomies of entities that are constantly evolving and to new scenarios with noise (for which there are few or no labelled data). recently, a new paradigm based on prompt-based learning (prompt engineering) has achieved great success in information extraction tasks by reformulating classification tasks as close questions @xcite @xcite . inspired by the advent of prompt engineering @xcite or specifically templatebased prompting methods @xcite , we propose to treat the ner task as (1) a ranking problem using the sequence-tosequence (seq2seq) framework, and (2) an extractive question-answering (qa) task. in the scope of our research (and due to several constraints), we focused only on five languages. in section 2, we detail the data in the five languages we chose to participate in this year and the prompt engineering-based methods for approaching ner in section 3. we then point out our findings and perform an error analysis in section 4. we present several similar works in section 5. finally, in section 6 we present our conclusions, future work and discuss the limitations of the proposed methods. ## data we utilized the multiconer ii corpus built by the organizers to challenge systems on the complex ner task @xcite , where entities are not only standard entity classes such as locations, persons, organizations, etc., but also titles of creative works or medical-related terms. the corpus comprises documents in twelve languages and a multilingual dataset corresponding to the concatenation of all monolingual datasets. entities are organized using a taxonomy of two levels, with six entries at the top level (coarse-grained) and 36 at the bottom level (fine-grained). however, in practice, only 33 are used. ## methodology in this section, we investigate the repercussion of semantically ambiguous and complex named entities on two prompt-based ner methods and their feasibility to be used in noisy and multilingual settings. to position the performance of the explored methods, we set a strong baseline, which has shown to be effective even on noisy datasets. ## conclusions in this paper, we investigated the performance of two prompt-based ner methods over five monolingual and multilingual datasets provided by the semeval-2023 task 2, multilingual complex named entity recognition (multiconer ii). our findings show that semantically ambiguous and complex entities in short and low-context settings are challenging for prompting techniques, affecting mostly their precision. these techniques were also influenced by the entity type taxonomy and context size, which increased the training and inference time. in future work, we will explore other methods to select proper templates and include knowledge bases to add informative context that helps discern the correct entity type.