A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction (2020)

Abstract: Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.

## introduction relation extraction (re) remains an important natural language processing task for understanding the interaction between entities that appear in texts. in supervised settings @xcite @xcite , obtaining finegrained relations for the biomedical domain is challenging due to not only the annotation costs, but the added requirement of domain expertise. distant supervision (ds), however, provides a meaningful way to obtain large-scale data for re @xcite , but this form of data collection also tends to result in an increased amount of noise, as the target relation may not always be expressed @xcite . exemplified in figure 1 , the last two sentences can be seen as potentially noisy evidence, as they do not explicitly express the given relation. since individual instance labels may be unknown @xcite , we instead build on the recent findings of @xcite and @xcite in using positional markings and latent relation direction (figure 1 ), as a signal to mitigate noise in bag-level multiple instance learning (mil) for distantly supervised biomedical re. our approach greatly simplifies previous work by @xcite with following contributions: in mil-based distant supervision for corpus-level re, earlier works rely on the assumption that at least one of the evidence samples represent the target relation in a triple @xcite @xcite . recently, piecewise convolutional neural networks (pcnn) @xcite have been applied to ds @xcite , with notable extensions in selective attention @xcite and the modelling of noise dynamics @xcite . @xcite proposed a joint learning framework for knowledge graph completion (kgc) and re with mutual attention, showing that ds improves downstream kgc performance, while kgc acts as an indirect signal to filter textual noise. @xcite extended this framework to biomedical re, using improved kgc models, complex @xcite and @xcite , as well as additional auxiliary tasks of entity-type classification and named entity recognition to mitigate noise. pre-trained language models, such as bert @xcite , have been shown to improve the downstream performance of many nlp tasks. relevant to distant re, @xcite extended the openai generative pre-trained transformer (gpt) model @xcite for bag-level mil with selective attention @xcite . @xcite enriched pre-training stage with kb entity information, resulting in improved performance. for sentence-level re, @xcite proposed an entity marking strategy for bert (referred to here as r-bert) to perform relation classification. specifically, they mark the entity boundaries with special tokens following the order they appear in the sentence. @xcite studied several data encoding schemes and found marking entity boundaries important for sentence-level re. with such encoding, they further proposed a novel pre-training scheme for distributed relational learning, suited to few-shot relation classification @xcite . our work builds on these findings, in particular, we extend the bert model @xcite for bag-level mil, similar to @xcite . more importantly, noting the significance of sentenceordered entity marking in sentence-level re @xcite , we introduce the knowledge-based entity marking strategy suited to bag-level ds. this naturally encodes the information stored in kb, reducing the inherent noise. ## conclusion this work extends bert to bag-level mil and introduces a simple data-driven strategy to reduce the noise in distantly supervised biomedical re. we note that the position of entities in sentence and the order in kb encodes the latent direction of relation, which plays an important role for learning under such noise. with a relatively simple methodology, we show that this can sufficiently be achieved by reducing the need for additional tasks and highlighting the importance of data quality.