Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023)

Abstract: Recently, the task of distantly supervised (DS) ultra-fine entity typing has received significant attention. However, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. This paper proposes a novel ultra-fine entity typing model with denoising capability. Specifically, we build a noise model to estimate the unknown labeling noise distribution over input contexts and noisy type labels. With the noise model, more trustworthy labels can be recovered by subtracting the estimated noise from the input. Furthermore, we propose an entity typing model, which adopts a bi-encoder architecture, is trained on the denoised data. Finally, the noise model and entity typing model are trained iteratively to enhance each other. We conduct extensive experiments on the Ultra-Fine entity typing dataset as well as OntoNotes dataset and demonstrate that our approach significantly outperforms other baseline methods.

## introduction entity typing is the task of identifying specific semantic types of entity mentions in given contexts. recently, more and more research has focused on ultra-fine entity typing @xcite @xcite . comparing to traditional entity typing tasks @xcite @xcite @xcite @xcite @xcite , the type set in ultra-fine entity typing is not restricted by kb schema, but includes a vast number of free-form types. to automatically annotate the large-scale ultrafine entity typing data, @xcite utilized different sources for distant supervision (ds), including: 1) entity linking, where they mine entity mentions that were linked to wikipedia in html, and extract relevant types from their encyclopedic definitions, and 2) head words, where they automatically extracted nominal head words from raw text as types. however, distant supervision often suffers from the low-precision and low-recall problems @xcite , where recall can suffer from kb or wikipedia incompleteness, and precision can suffer when the selected types do not fit the context. ## conclusion in this paper, we aim to improve the performance of ultra-fine entity typing by explicitly denoising the ds data. noise modeling is our key component to denoise, where the model fully explores the correlation between the query context and assigned noisy type set, and outputs the estimated noise. to train the noise model, we perturb on the small-scale gold dataset to mimic the noise distribution on ds instances. furthermore, we utilize the large-scale ds data as weak supervision to train our noise model. the entity typing model is then trained on the gold data set and denoised ds dataset. experimental results empirically prove the effectiveness of our method on handling distantly supervised ultra-fine entity typing.