Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition (2022)

Abstract: Distantly-supervised named entity recognition (NER) locates and classifies entities using only knowledge bases and unlabeled corpus to mitigate the reliance on human-annotated labels. The distantly annotated data suffer from the noise in labels, and previous works on DSNER have proved the importance of pre-refining distant labels with hand-crafted rules and extra existing semantic information. In this work, we explore the way to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing these annotations in contrastive learning frameworks. the proposed distant label refinement model can give modified suggestions on distant data without additional supervised labels, and thus reduces the requirement on the quality of the knowledge bases. We perform extensive experiments and observe that recent and state-of-the-art DSNER methods gain evident benefits with our method.

## introduction named entity recognition (ner) refers to the sequence tagging task of detecting the interested entities in unstructured texts and classifying them into predefined categories. ner serves as a foundation part of information extraction in natural language processing (nlp) with applications in many downstream tasks such as question answering @xcite , knowledge graph construction @xcite , and dialog systems @xcite . supervised ner models have been developing rapidly in recent years and have achieved enormous success @xcite . however, acquiring abundant high-quality human annotations where every word within a sentence should be labeled can be very expensive and limits the application of ner models in many domains. ## methods in this section, we detailedly describe the whole pipeline of credel (figure 1 ). firstly, a knowledge base is used to obtain the distantly labeled tagging sequence. then we construct sample pairs on a large corpus and train a scoring model via contrastive learning. we also introduce a module to generate high-quality positive samples by modifying the entity boundary. for the purpose of improving the quality of distant labeled training data, we apply the scoring model to modify datasets used by existing dsner models, leading to their better performances. ## ablation study to verify the effectiveness of each design of cre-del, we conduct the ablation studies. we mainly discuss three parts, the first of which is the effectiveness of parse and it has been fully exploited in the results part. the second ablation is credel w/o typing, that is, without the final entity typing module. the other part of ablation considers the selection of training sample cases. we train cre-del (1) w. (y 1,p , y 1,n ), using only sample pair (y 1,p , y 1,n ) in section 3.2 and easy samples; (2) w. easy only, as no hard samples are included; and (3) w. hard only, where only easy samples are used. the results are shown in finally we discuss the condition where the parser does not act on the training set of credel but on the output of dsner models without credel, denoted as method w. parse after. as an automatic refinement model, we should outperform the baseline refinement with parser. we apply the same parse-enhancing rule and find the f1-score is far behind credel as demonstrated in ## conclusion this paper proposes a novel approach to automatically learn the refinement knowledge of distantly annotated ner labels and modify the distant labels to enhance dsner models. the proposed method consists of a contrastive samples generation module, a contrastive training procedure, and a distantly label improving strategy. experiments demonstrate that our method consistently and significantly improves dsner and ner models on distantly annotated ner data, and it can be stably applied to all the datasets and methods.