Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger (2022)

Abstract: “This paper introduces DepTrigger, a simple and effective model in low-resource named entity recognition (NER) based on multi-hop dependency triggers. Dependency triggers refer to salient nodes relative to an entity in the dependency graph of a context sentence. Our main observation is that triggers generally play an important role in recognizing the location and the type of entity in a sentence. Instead of exploiting the manual labeling of triggers, we use the syntactic parser to annotate triggers automatically. We train DepTrigger using an independent model architectures which are Match Network encoder and Entity Recognition Network encoder. Compared to the previous model TriggerNER, DepTrigger outperforms for long sentences, while still maintain good performance for short sentences as usual. Our framework is significantly more cost-effective in real business.”

## introduction named entity recognition (ner) aims to detect the span from text belonging to the semantic category such as person, location, organization, etc. ner plays a core component in many nlp tasks and is widely employed in downstream applications, such as knowledge graph @xcite , question answering @xcite and dialogue system @xcite . the deep-learning based approaches have shown remarkable success in ner, while it requires large corpora annotated with named entities. moreover, in many practical settings, we wish to apply ner to domains with a very limited amount of labeled data since annotating data is a labor-intensive and time-consuming task. therefore, it is an emergency to improve the performance of the deep-learning based ner model with limited labeled data. previous work in low-resource ner mainly focused on meta-learning @xcite , distantly supervision @xcite , transfer learning @xcite proposed an approach based on entity trigger called triggerner. the key idea is that an entity trigger is a group of words that can help explain the recognition process of an entity in a sentence. considering the sentence "biden is the president of ", we are able to infer that there is a country entity on the underline according to "the president of". in this case, "the president of" is a group of triggers. experiments reveal that the performance of utilizing 20% of the trigger-annotated sentences is comparable to that of exploiting 70% of conventional annotated sentences. however, crowd-sourced entity trigger annotations, which suffer from the same problem as traditional annotation, require labor costs and expert experience. inspired by attribute triggers in attribute extraction (huang2, 2017), this paper presents an alternative approach to automatically annotate the trigger in a sentence by utilizing the syntactic parse. fig. 1 is the dependency parse result of the sentence "alice was born in beijing", the relation "nsubj:pass" shows that the subject of "born" is "alice". according to the meaning of "born", we are capable of inferring that "alice" is a person entity. inspired by this fact, we propose a novel model, namley deptrigger, which explore the structures of dependency trees and utilize the syntactic parser to annotate trigger in a sentence. figure 1 : the dependency parse results of "alice was born in beijing", "s-per" is entity label, "t-trigger" is trigger label, "o" denotes others. naturally, we propose a simple yet effective framework for low-resource ner, namely deptrig-gerner. it includes a trigger semantic matching module (trigger match network) and a sequence annotation module (entity recognition network). the deptriggerner adopts two-steps pipeline mode: 1) we first trains the trigger match network module for learning trigger representation; and 2) we combine trigger representation to train the entity recognition network module. our main contribution includes the new proposed "deptrigger" model, which reduces the cost and complexity by using a syntactic parser to automatically annotate trigger. we evaluate deptrigger on conll2003 (erik, 2003) and bc5cdr (li, 2016), where deptrigger outperforms the triggerner model on bc5cdr but slightly under-performs on conll2003. compared to triggerner, deptrigger is particularly useful in its ability to automatically produce annotated triggers. besides, the independent model architectures have a better performance. our results suggest that deptrigger is a promising alternative to the triggerner in low-resource ner tasks. ## conclusion and future work we have introduced dependency trigger to incorporate trigger information into ner method. the core of our method is using syntactic parser to automatically label the trigger of entities. our model performs well for long sentences, while maintain similar performance as triggerner for short sentences. thanks to automatically annotate trigger of entities, our framework is more practical in the real business. future work with deptrigger includes: 1) adjusting our model to encoder based on language model; 2) making a further analysis of trigger type; 3) developing models for improving the performance on short sentences.