Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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Learning to Transfer Relational Representations through Analogy

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330110015 ◽

2019 ◽

Vol 33 ◽

pp. 10015-10016

Author(s):

Gaetano Rossiello ◽

Alfio Gliozzo ◽

Michael Glass

Keyword(s):

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

The State ◽

Large Set ◽

Relational Information ◽

Siamese Network ◽

Distant Supervision ◽

Novel Approach ◽

Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

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Biomedical Relation Extraction Using Distant Supervision

Scientific Programming ◽

10.1155/2020/8893749 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Nada Boudjellal ◽

Huaping Zhang ◽

Asif Khan ◽

Arshad Ahmad

Keyword(s):

Big Data ◽

Information Extraction ◽

State Of The Art ◽

Relation Extraction ◽

Knowledge Bases ◽

Structured Data ◽

Distant Supervision ◽

Future Challenges ◽

Unstructured Information ◽

Biomedical Relation Extraction

With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information into an easily interpretable structured data. Relation extraction is the second of the two important tasks of relation extraction. This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state-of-the-art work that proposed this method. Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner practitioners seeking knowledge on this subject. Moreover, the limitations of the proposed approaches and future challenges were highlighted, and possible solutions were proposed.

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Towards relation extraction from Arabic text: a review

International Robotics & Automation Journal ◽

10.15406/iratj.2019.05.00195 ◽

2019 ◽

Vol 5 (5) ◽

pp. 212-215

Author(s):

Abeer AlArfaj

Keyword(s):

Language Processing ◽

Question Answering ◽

State Of The Art ◽

Relation Extraction ◽

Arabic Language ◽

Extraction Methods ◽

The State ◽

Semantic Relations ◽

Arabic Text ◽

Taxonomic Relation

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

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Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study

JMIR Medical Informatics ◽

10.2196/17644 ◽

2020 ◽

Vol 8 (5) ◽

pp. e17644

Author(s):

Xiaofeng Liu ◽

Jianye Fan ◽

Shoubin Dong

Keyword(s):

State Of The Art ◽

Relation Extraction ◽

Biomedical Literature ◽

Long Distance ◽

Replacement Method ◽

Replacement Algorithm ◽

Additional Noise ◽

The Relationship ◽

Document Level ◽

Biomedical Relation Extraction

Background The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. Objective This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction. Methods This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics. Results Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance. Conclusions When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.

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Kernelized Hashcode Representations for Relation Extraction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016431 ◽

2019 ◽

Vol 33 ◽

pp. 6431-6440

Author(s):

Sahil Garg ◽

Aram Galstyan ◽

Greg Ver Steeg ◽

Irina Rish ◽

Guillermo Cecchi ◽

...

Keyword(s):

Kernel Methods ◽

State Of The Art ◽

Computational Cost ◽

Relation Extraction ◽

Locality Sensitive Hashing ◽

Classification Methods ◽

Classification Problems ◽

Random Subspaces ◽

Class Labels ◽

Biomedical Relation Extraction

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-ofthe-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.

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Are Noisy Sentences Useless for Distant Supervised Relation Extraction?

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6407 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8799-8806

Author(s):

Yuming Shang ◽

He-Yan Huang ◽

Xian-Ling Mao ◽

Xin Sun ◽

Wei Wei

Keyword(s):

Selective Attention ◽

State Of The Art ◽

Relation Extraction ◽

The State ◽

Benchmark Dataset ◽

Experimental Results ◽

Training Data ◽

High Confidence ◽

Feature Representations ◽

Novel Method

The noisy labeling problem has been one of the major obstacles for distant supervised relation extraction. Existing approaches usually consider that the noisy sentences are useless and will harm the model's performance. Therefore, they mainly alleviate this problem by reducing the influence of noisy sentences, such as applying bag-level selective attention or removing noisy sentences from sentence-bags. However, the underlying cause of the noisy labeling problem is not the lack of useful information, but the missing relation labels. Intuitively, if we can allocate credible labels for noisy sentences, they will be transformed into useful training data and benefit the model's performance. Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. Specifically, our model contains three modules: a sentence encoder, a noise detector and a label generator. The sentence encoder is used to obtain feature representations. The noise detector detects noisy sentences from sentence-bags, and the label generator produces high-confidence relation labels for noisy sentences. Extensive experimental results demonstrate that our model outperforms the state-of-the-art baselines on a popular benchmark dataset, and can indeed alleviate the noisy labeling problem.

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Joint Extraction of Entities and Relations Based on a Novel Graph Scheme

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/620 ◽

2018 ◽

Cited By ~ 11

Author(s):

Shaolei Wang ◽

Yue Zhang ◽

Wanxiang Che ◽

Ting Liu

Keyword(s):

Directed Graph ◽

State Of The Art ◽

Relation Extraction ◽

The State ◽

The Other ◽

Joint Learning ◽

Joint Decoding ◽

Joint Task ◽

Art Methods ◽

Parameter Sharing

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. Most existing neural joint methods extract entities and relations separately and achieve joint learning through parameter sharing, leading to a drawback that information between output entities and relations cannot be fully exploited. In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations. Experiments on NewYork Times (NYT) corpora show that our approach outperforms the state-of-the-art methods.

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Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion

Applied Sciences ◽

10.3390/app11167318 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7318

Author(s):

Xian Zhu ◽

Lele Zhang ◽

Jiangnan Du ◽

Zhifeng Xiao

Keyword(s):

Language Processing ◽

Domain Knowledge ◽

Learning Algorithm ◽

State Of The Art ◽

Relation Extraction ◽

Text Representation ◽

Upper Level ◽

Document Level ◽

Semantic Interaction ◽

Biomedical Relation Extraction

Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In the biomedical field, building an efficient and accurate RE system is critical for the construction of a domain knowledge base to support upper-level applications. Recent advances have witnessed a focus shift from sentence to document-level RE problems, which are more challenging due to the need for inter- and intra-sentence semantic reasoning. This type of distant dependency is difficult to understand and capture for a learning algorithm. To address the challenge, prior efforts either attempted to improve the cross sentence text representation or infuse domain or local knowledge into the model. Both strategies demonstrated efficacy on various datasets. In this paper, a keyword-attentive knowledge infusion strategy is proposed and integrated into BioBERT. A domain keyword collection mechanism is developed to discover the most relation-suggestive word tokens for bio-entities in a given context. By manipulating the attention masks, the model can be guided to focus on the semantic interaction between bio-entities linked by the keywords. We validated the proposed method on the Biocreative V Chemical Disease Relation dataset with an F1 of 75.6%, outperforming the state-of-the-art by 5.6%.

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Temporal Relation Extraction in Clinical Texts

ACM Computing Surveys ◽

10.1145/3462475 ◽

2022 ◽

Vol 54 (7) ◽

pp. 1-36

Author(s):

Yohan Bonescki Gumiel ◽

Lucas Emanuel Silva e Oliveira ◽

Vincent Claveau ◽

Natalia Grabar ◽

Emerson Cabrera Paraiso ◽

...

Keyword(s):

Task Complexity ◽

State Of The Art ◽

Relation Extraction ◽

Clinical Findings ◽

The State ◽

Temporal Relation ◽

Medical Specialties ◽

Clinical Text ◽

Temporal Expressions ◽

Temporal Relation Extraction

Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages.

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