A shape composition method for named entity recognition

Abstract

Large language models (LLMs) roughly encode a sentence into a dense representation (a vector), which mixes up the semantic expression of all named entities within a sentence. So the decoding process is easily overwhelmed by sentence-specific information learned during the pre-training process. It results in seriously performance degeneration in recognizing named entities, especially annotated with nested structures. In contrast to LLMs condensing a sentence into a single vector, our model adopts a discriminative language model to map each sentence into a high-order semantic space. In this space, named entities are decomposed into entity body and entity edge. The decomposition is effective to decode complex semantic structures of named entities. In this paper, a shape composition method is proposed for recognizing named entities. This approach leverages a multi-objective learning neural architecture to simultaneously detect entity bodies and classify entity edges. During training, the dual objectives for body and edge learning guide the deep network to encode more task-relevant semantic information. Our method is evaluated on eight widely used public datasets and demonstrated competitive performance. Analytical experiments show that the strategy of let semantic expressions take its course aligns with the entity recognition task. This approach yields finer-grained semantic representations, which enhance not only NER but also other NLP tasks.