Nested Named Entity Recognition via Second-best Sequence Learning and Decoding (2020)

Abstract: When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.

## introduction named entity recognition (ner) is the task of identifying text spans associated with proper names and classifying them according to their semantic class such as person or organization. ner, or in general the task of recognizing entity mentions, is one of the first stages in deep language understanding, and its importance has been well recognized in the nlp community @xcite . one popular approach to the ner task is to regard it as a sequence labeling problem. in this case, it is implicitly assumed that mentions are not nested in texts. however, names often contain entities nested within themselves, as illustrated in figure 1 , which contains 3 mentions of the same type (protein) in the span ''... in ca2+ -dependent pkc isoforms in ...'', taken from the genia dataset @xcite . name nesting is common, especially in technical domains @xcite @xcite . the assumption of no nesting leads to loss of potentially important information and may negatively impact subsequent downstream tasks. for instance, a downstream entity linking system that relies on ner may fail to link the correct entity if the entity mention is nested. various approaches to recognizing nested entities have been proposed. many of them rely on producing and rating all possible (sub)spans, which can be computationally expensive. @xcite provided a hypergraph-based approach to consider all possible spans. @xcite proposed a neural exhaustive model that enumerates and classifies all possible spans. these methods, however, achieve high performance at the cost of time complexity. to reduce the running time, they set a threshold to discard longer entity mentions. if the hyperparameter is set low, running time is reduced but longer mentions are missed. in contrast, muis and @xcite proposed a sequence labeling approach that assigns tags to gaps between words, which efficiently handles sequences using viterbi decoding. however, this approach suffers from structural ambiguity issues during inference, as explained by @xcite . @xcite proposed another hypergraph-based approach that learns the structure in a greedy manner. however, their method uses an additional hyperparameter as the threshold for selecting multiple mention candidates. this hyperparameter affects the trade-off between recall and precision. in this paper, we propose new learning and decoding methods to extract nested entities without any additional hyperparameters. we summarize our contributions as follows: ## method we propose applying conditional random fields (crfs) @xcite , which is commonly used for flat ner @xcite @xcite @xcite , to nested ner in this study. we first explain our usage of crf, which is the base of our decoding and training methods. then, we introduce our decoding and training methods. our decoding and training methods focus on the output layer of neural architectures and therefore can be combined with any neural model. ## conclusion we propose learning and decoding methods for extracting nested entities. our decoding method iteratively recognizes entities from outermost ones to inner ones in an outside-to-inside way. it recursively searches a span of each extracted entity for nested entities with second-best sequence decoding. we also design an objective function for training that ensures our decoding algorithm. our method has no hyperparameters beyond those of conventional crf-based models. our method achieves 85.82%, 84.34%, and 77.36% f1-scores on ace-2004, ace-2005, and genia datasets, respectively. for future work, one interesting direction is joint modeling of ner with entity linking or coreference resolution. previous studies @xcite @xcite demonstrated that leveraging mutual dependency of the ner, linking, and coreference tasks could boost each performance. we would like to address this issue while taking nested entities into account.