CSECU - DSG at S em E val-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach (2022)

Abstract: Recognizing complex and ambiguous named entities (NEs) is one of the formidable tasks in the NLP domain. However, the diversity of linguistic constituents, syntactic structure, semantic ambiguity as well as differences from traditional NEs make it challenging to identify the complex NEs. To address these challenges, SemEval-2022 Task 11 introduced a shared task MultiCoNER focusing on complex named entity recognition in multilingual settings. This paper presents our participation in this task where we propose two different approaches including a BiLSTM-CRF model with stacked-embedding strategy and a transformer-based approach. Our proposed method achieved competitive performance among the participants’ methods in a few languages.

## introduction named entity recognition (ner) is a popular sequence labeling task in the natural language processing (nlp) arena. it has numerous applications in several computational linguistic tasks including designing efficient search systems, data mining, and document indexing. however, prior studies mostly focused on identifying traditional named entities (nes) recognition e.g. person names, locations, and organizations names @xcite . the ever-growing generation of unstructured social media data contains a huge amount of complex @xcite and ambiguous @xcite named entities. this is because social media data is severely induced by noise as well as the linguistic constituent, syntactic and semantic ambiguity exists in this data source. besides, the social media data mostly have multilingual data. therefore it poses new challenges to the traditional named entity recognizer system (aguilar **the first two authors have equal contributions. @xcite . to address the challenges of recognizing such complex and ambiguous named entities in multilingual settings, @xcite introduced a shared task at semeval-2022 named as multiconer. the task is composed of three category of tracks including multi-lingual, mono-lingual, and code-mixed tracks. a multilingual dataset @xcite containing data from 11 languages is used to assess the participants' system. to illustrate a clear view of the task definition, we articulate two examples from english languages and corresponding labels in ## proposed framework in this section, we describe our proposed approach for the multiconer shared task. our goal is to identify the complex and ambiguous named entities in multilingual settings. the task is articulated into multi-lingual, mono-lingual, and code-mixed tracks. to address the task challenges, we employ two different approaches including a bilstm-crf ## conclusion and future directions in this paper, we presented our proposed systems to address the challenges of the multiconer shared task. we employed a bilstm-crf with a stacked embedding-based approach and a transformerbased approach. experimental results demonstrate that transformer-based approach performed better compared to the other approach in most of the languages dataset. in the future, we have a plan to incorporate the task-specific features and technologies to address the challenges properly. we also have a plan to explore the existing ner technologies and fuse them in a unified architecture to overcome the limitations of the current approaches.