DeepAI
Log In Sign Up

Domain-Specific NER via Retrieving Correlated Samples

08/27/2022
by   Xin Zhang, et al.
0

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts are also difficult for human annotators. In fact, we can obtain some potentially helpful information from correlated texts, which have some common entities, to help the text understanding. Then, one can easily reason out the correct answer by referencing correlated samples. In this paper, we suggest enhancing NER models with correlated samples. We draw correlated samples by the sparse BM25 retriever from large-scale in-domain unlabeled data. To explicitly simulate the human reasoning process, we perform a training-free entity type calibrating by majority voting. To capture correlation features in the training stage, we suggest to model correlated samples by the transformer-based multi-instance cross-encoder. Empirical results on datasets of the above two domains show the efficacy of our methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/13/2021

ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

Named entity recognition (NER) is an important task that aims to resolve...
03/13/2022

ProtagonistTagger – a Tool for Entity Linkage of Persons in Texts from Various Languages and Domains

Named entities recognition (NER) and disambiguation (NED) can add semant...
06/04/2021

Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain

The human resource (HR) domain contains various types of privacy-sensiti...
10/26/2020

Using Unlabeled Texts for Named-Entity Recognition

Named Entity Recognition (NER) poses the problem of learning with multip...
12/01/2016

Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings

Content on the Internet is heterogeneous and arises from various domains...
08/05/2022

A Noise-Robust Loss for Unlabeled Entity Problem in Named Entity Recognition

Named Entity Recognition (NER) is an important task in natural language ...