Relation Extraction for the Food Domain without Labeled Training Data – Is Distant Supervision the Best Solution?

AbstractSignificant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.

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Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.3301419 ◽

2019 ◽

Vol 33 ◽

pp. 419-426 ◽

Cited By ~ 6

Author(s):

Yujin Yuan ◽

Liyuan Liu ◽

Siliang Tang ◽

Zhongfei Zhang ◽

Yueting Zhuang ◽

...

Keyword(s):

Selective Attention ◽

Supervised Learning ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Training Data ◽

Distant Supervision ◽

Sentence Level ◽

Noise Robust

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.

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PEDL: extracting protein–protein associations using deep language models and distant supervision

Bioinformatics ◽

10.1093/bioinformatics/btaa430 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i490-i498

Author(s):

Leon Weber ◽

Kirsten Thobe ◽

Oscar Arturo Migueles Lozano ◽

Jana Wolf ◽

Ulf Leser

Keyword(s):

Relation Extraction ◽

Training Data ◽

Language Models ◽

Supplementary Information ◽

Functional Protein ◽

Major Pathway ◽

Distant Supervision ◽

Order Of Magnitude ◽

Biomedical Publications ◽

Pathway Databases

Abstract Motivation A significant portion of molecular biology investigates signalling pathways and thus depends on an up-to-date and complete resource of functional protein–protein associations (PPAs) that constitute such pathways. Despite extensive curation efforts, major pathway databases are still notoriously incomplete. Relation extraction can help to gather such pathway information from biomedical publications. Current methods for extracting PPAs typically rely exclusively on rare manually labelled data which severely limits their performance. Results We propose PPA Extraction with Deep Language (PEDL), a method for predicting PPAs from text that combines deep language models and distant supervision. Due to the reliance on distant supervision, PEDL has access to an order of magnitude more training data than methods solely relying on manually labelled annotations. We introduce three different datasets for PPA prediction and evaluate PEDL for the two subtasks of predicting PPAs between two proteins, as well as identifying the text spans stating the PPA. We compared PEDL with a recently published state-of-the-art model and found that on average PEDL performs better in both tasks on all three datasets. An expert evaluation demonstrates that PEDL can be used to predict PPAs that are missing from major pathway databases and that it correctly identifies the text spans supporting the PPA. Availability and implementation PEDL is freely available at https://github.com/leonweber/pedl. The repository also includes scripts to generate the used datasets and to reproduce the experiments from this article. Supplementary information Supplementary data are available at Bioinformatics online.

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Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6236 ◽

2020 ◽

Vol 34 (05) ◽

pp. 7407-7414

Author(s):

Trapit Bansal ◽

Pat Verga ◽

Neha Choudhary ◽

Andrew McCallum

Keyword(s):

State Of The Art ◽

Relation Extraction ◽

Training Data ◽

Biomedical Text ◽

Entity Linking ◽

Distant Supervision ◽

Entity Relationships ◽

And Training

Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision – which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system.

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Exploiting Parallel News Streams for Unsupervised Event Extraction

Transactions of the Association for Computational Linguistics ◽

10.1162/tacl_a_00127 ◽

2015 ◽

Vol 3 ◽

pp. 117-129 ◽

Cited By ~ 2

Author(s):

Congle Zhang ◽

Stephen Soderland ◽

Daniel S. Weld

Keyword(s):

Graphical Model ◽

Relation Extraction ◽

Event Extraction ◽

Training Data ◽

Natural Language Text ◽

Distant Supervision ◽

Wide Range ◽

Precision Recall Curve ◽

Language Text ◽

Better Than

Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations ( e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open challenge. This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.

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Bias Modeling for Distantly Supervised Relation Extraction

Mathematical Problems in Engineering ◽

10.1155/2015/969053 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yang Xiang ◽

Yaoyun Zhang ◽

Xiaolong Wang ◽

Yang Qin ◽

Wenying Han

Keyword(s):

Language Processing ◽

Learning Algorithm ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Training Data ◽

Free Text ◽

Distant Supervision ◽

Annotation Process ◽

Noise Tolerant

Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1)bias-distto model the relative distance between points (instances) and classes (relation centers); (2)bias-rewardto model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models:MIML-dist,MIML-dist-classify, andMIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.

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