Domain-Specific NER via Retrieving Correlated Samples (2022)

Abstract: 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.

## introduction named entity recognition (ner), which first locates entity positions and then labels their types sequentially, is a fundamental topic in both academia and industry @xcite . normal ner models consider the input samples to be independent of each other, learning the common intra-instance patterns and making predictions in a sequential way. this paradigm has shown surprising successes in decades, especially with the help of emerging deep learning @xcite @xcite @xcite @xcite . however, learned models will fail at some hard cases, which would be inevitably encountered in figure 1 : an address example with retrieved texts. the model incorrectly predicted "白城镇 (baicheng town)" and "赉县火车站 (lai county train station)" because they match common patterns, i.e. "xx镇" (xx town) and "x县火车站" (x county train station). "吉林" is ambiguous, which is both a province and a city. learning from correlated or nested data is mainly studied in machine learning and computer vision @xcite @xcite . images in sub-groups naturally show a high degree of correlation on both features and labels, and come with nested structures @xcite , such as different ## conclusion in this work, we investigated utilizing naturally correlated samples to improve current ner models on the chinese address and e-commerce domain. we propose to retrieve correlated samples for the given text by the bm25 and elasticsearch engine. to explore the correlations in a light way, we suggest calibrating the predicted entity types by cross-instance entity voting. to further incorporate these correlated samples into model training, we use multi-instance cross-encoders to learn more complex correlations. empirical results show that the painless entity type calibrating improved the performance to some extent, and modeling correlations by cross-encoders achieved the state-of-theart performance. we hope this idea could benefit the similar scenario/domains of other tasks. we will release our code and data at github.com/izhx/ner-unlabeled-data-retrieval to facilitate future research.