Hierarchical Region Learning for Nested Named Entity Recognition (2020)

Abstract: Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-the-art baselines.

## introduction as a fundamental information extraction task, named entity recognition (ner) is widely used in various downstream tasks, such as entity linking and entity search. most studies assigns a label to each token of the sequence for the flat ner problem @xcite . however, it is common that entities are embedded in other entities in many domains @xcite . example from ace-2005 dataset shown in fig. 1 illustrates that the top-level per entity includes a nested entity with org label. how to recognize all entities recursively from innermost to outermost is referred to as the nested ner problem. existing approaches mainly solve the nested ner problem by classifying all candidate subsequences (a.k.a regions). the key to region based methods lies in candidate region detection. one kind is the brute force method @xcite to enumerate all possible o(n 2 ) subsequences for each sentence with n words. the other kind @xcite is to generate and classify candidate regions in a two-stage paradigm, often leading to cascaded errors. thus region based methods face efficiency and effectiveness challenges. to tackle these challenges, we propose a hierarchical region learning framework, referred to as hire. first, inspired by constituent parsing tree as the top of fig. 1 and its neural syntactic distance @xcite , we introduce the coherence measure between adjacent regions. then we generate a region tree for each sentence by merging two adjacent regions recursively based on this region coherence measure in a bottom-up manner. finally, hierarchical regions are classified based on the boundary and merging word representation. we train the hierarchical region generation and classification tasks simultaneously. experimental results on three benchmark datasets ace-2005, genia and jnlpba demonstrate that hire shows the competitive or better performance than baselines. hire generates only o(n) candidate regions about 77.9% less than the brute-force method and achieves 98.1% true region recall in the genia dataset, a good trade-off between efficiency and effectiveness. ## related work given a sentence of n words (w 1 , . . . , w n ), the nested named entity recognition task aims at identifying all the entities especially when one entity subsequence (w i , . . . , w j ), @xmath0, . . . , w q ), @xmath1. according to reduced different problems, existing nested ner models mainly fall into three categories. sequence labeling models assign multiple labels to each word assuming that one word may belong to multiple entities, such as linearization method @xcite and layered crf @xcite . structured label classification models capture the label relationship of a sentence for better performance. @xcite proposed hyper-graphs models to describe the label relationship, and either human designed or latent features were adopted for classification. region based models were summarized by @xcite as obtaining all possible regions and assigning labels to regions. the key to region classification models is how to obtain candidate regions from a sentence. one is the bruteforce method @xcite , which enumerates all subsequences of a sentence for classification with high time complexity. the other is to formulate the task as a two-stage paradigm. @xcite detected a small set of candidate regions with high efficiency, but only about 80% entities could be found in the first stage, making a performance bottleneck. some studies @xcite leveraged the external knowledge, such as constituent parsing tree, to guide the first step, which achieved impressive performance but suffered from the cubic time complexity and error propagation from external tools. most methods above represented the region as the average or weighted sum of word representations, ignoring the region structure. ## methods to tackle efficiency and effectiveness challenges in region based methods, we propose a hierarchical region learning framework for nested ner problem, namely hire in fig. 2 . ## experiments to investigate the effectiveness and efficiency of our proposed method, we conduct comprehensive experiments on three benchmark ner datasets. ## conclusion leading region based approaches to nested ner face the efficiency and effectiveness challenges. we propose a hierarchical region framework to generate hierarchical regions and assign those regions with hierarchical representation an entity categorical label together. experimental results demonstrate a significant improvement of our proposed framework in terms of efficiency and effectiveness than sota baselines. in future work, how to represent hierarchical regions better will be considered.