scholarly journals RCorp: a resource for chemical disease semantic extraction in Chinese

2019 ◽  
Vol 19 (S5) ◽  
Author(s):  
Yueping Sun ◽  
Li Hou ◽  
Lu Qin ◽  
Yan Liu ◽  
Jiao Li ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Combination Therapy ◽  
Medical Literature ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Agreement Score ◽  
Disease Entities ◽  
Result Analysis ◽  
Synergistic Combinations ◽  
Disease Specific

Abstract Background To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated. Methods In this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process. Results The resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. Conclusions We extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

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.

scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

scholarly journals Information Extraction of Cybersecurity Concepts: An LSTM Approach

2019 ◽  
Vol 9 (19) ◽  
pp. 3945 ◽  
Author(s):  
Houssem Gasmi ◽  
Jannik Laval ◽  
Abdelaziz Bouras
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Network Models ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Feature Engineering ◽  
Neural Network Models ◽  
Feature Based

Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the properties of entities, domain knowledge, entity context, and linguistic characteristics. Therefore, to alleviate the need for feature engineering, we propose the usage of neural network models, specifically the long short-term memory (LSTM) models to accomplish the tasks of Named Entity Recognition (NER) and Relation Extraction (RE). We evaluated the proposed models on two tasks. The first task is performing NER and evaluating the results against the state-of-the-art Conditional Random Fields (CRFs) method. The second task is performing RE using three LSTM models and comparing their results to assess which model is more suitable for the domain of cybersecurity. The proposed models achieved competitive performance with less feature-engineering work. We demonstrate that exploiting neural network models in cybersecurity text mining is effective and practical.

scholarly journals Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Guimin Huang ◽  
Jianheng Chen ◽  
Yabing Wang
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Background Knowledge ◽  
Word Embedding ◽  
Entity Recognition ◽  
Instance Selection ◽  
Attention Model ◽  
Training Tool ◽  
Knowledge Based ◽  
Proposed Model

Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.

scholarly journals RUREBUS-2020 SHARED TASK: RUSSIAN RELATION EXTRACTION FOR BUSINESS

2020 ◽  
Author(s):  
V. A. Ivanin ◽  
◽  
E. L. Artemova ◽  
T. V. Batura ◽  
V. V. Ivanov ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Relation Extraction ◽  
Business Case ◽  
Entity Recognition ◽  
Quality Data ◽  
Shared Task ◽  
Annotation Scheme ◽  
Data Set ◽  
Named Entity ◽  
Short Time

In this paper, we present a shared task on core information extraction problems, named entity recognition and relation extraction. In contrast to popular shared tasks on related problems, we try to move away from strictly academic rigor and rather model a business case. As a source for textual data we choose the corpus of Russian strategic documents, which we annotated according to our own annotation scheme. To speed up the annotation process, we exploit various active learning techniques. In total we ended up with more than two hundred annotated documents. Thus we managed to create a high-quality data set in short time. The shared task consisted of three tracks, devoted to 1) named entity recognition, 2) relation extraction and 3) joint named entity recognition and relation extraction. We provided with the annotated texts as well as a set of unannotated texts, which could of been used in any way to improve solutions. In the paper we overview and compare solutions, submitted by the shared task participants. We release both raw and annotated corpora along with annotation guidelines, evaluation scripts and results at https://github.com/dialogue-evaluation/RuREBus.

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