• Title, Summary, Keyword: Relation Extraction

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Relation Extraction based on Extended Composite Kernel using Flat Lexical Features (평면적 어휘 자질들을 활용한 확장 혼합 커널 기반 관계 추출)

  • Chai, Sung-Pil;Jeong, Chang-Hoo;Chai, Yun-Soo;Myaeng, Sung-Hyon
    • Journal of KIISE:Software and Applications
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    • v.36 no.8
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    • pp.642-652
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    • 2009
  • In order to improve the performance of the existing relation extraction approaches, we propose a method for combining two pivotal concepts which play an important role in classifying semantic relationships between entities in text. Having built a composite kernel-based relation extraction system, which incorporates both entity features and syntactic structured information of relation instances, we define nine classes of lexical features and synthetically apply them to the system. Evaluation on the ACE RDC corpus shows that our approach boosts the effectiveness of the existing composite kernels in relation extraction. It also confirms that by integrating the three important features (entity features, syntactic structures and contextual lexical features), we can improve the performance of a relation extraction process.

Meta Learning based Global Relation Extraction trained by Traditional Korean data (전통 문화 데이터를 이용한 메타 러닝 기반 전역 관계 추출)

  • Kim, Kuekyeng;Kim, Gyeongmin;Jo, Jaechoon;Lim, Heuiseok
    • Journal of the Korea Convergence Society
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    • v.9 no.11
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    • pp.23-28
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    • 2018
  • Recent approaches to Relation Extraction methods mostly tend to be limited to mention level relation extractions. These types of methods, while featuring high performances, can only extract relations limited to a single sentence or so. The inability to extract these kinds of data is a terrible amount of information loss. To tackle this problem this paper presents an Augmented External Memory Neural Network model to enable Global Relation Extraction. the proposed model's Global relation extraction is done by first gathering and analyzing the mention level relation extraction by the Augmented External Memory. Additionally the proposed model shows high level of performances in korean due to the fact it can take the often omitted subjects and objectives into consideration.

Relation Extraction Using Convolution Tree Kernel Expanded with Entity Features

  • Qian, Longhua;Zhou, Guodong;Zhu, Qiaomin;Qian, Peide
    • Proceedings of the Korean Society for Language and Information Conference
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    • pp.415-421
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    • 2007
  • This paper proposes a convolution tree kernel-based approach for relation extraction where the parse tree is expanded with entity features such as entity type, subtype, and mention level etc. Our study indicates that not only can our method effectively capture both syntactic structure and entity information of relation instances, but also can avoid the difficulty with tuning the parameters in composite kernels. We also demonstrate that predicate verb information can be used to further improve the performance, though its enhancement is limited. Evaluation on the ACE2004 benchmark corpus shows that our system slightly outperforms both the previous best-reported feature-based and kernel-based systems.

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Minimally Supervised Relation Identification from Wikipedia Articles

  • Oh, Heung-Seon;Jung, Yuchul
    • Journal of Information Science Theory and Practice
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    • v.6 no.4
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    • pp.28-38
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    • 2018
  • Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

A Novel Recognition Algorithm Based on Holder Coefficient Theory and Interval Gray Relation Classifier

  • Li, Jingchao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.11
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    • pp.4573-4584
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    • 2015
  • The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

Improving accessibility and distinction between negative results in biomedical relation extraction

  • Sousa, Diana;Lamurias, Andre;Couto, Francisco M.
    • Genomics & Informatics
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    • v.18 no.2
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    • pp.20.1-20.4
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    • 2020
  • Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of research hypotheses. However, most biomedical relation extraction datasets do not seek to distinguish between a false and a negative relation among two biomedical entities. Furthermore, datasets created using distant supervision techniques also have some false negative relations that constitute undocumented/ unknown relations (missing from a knowledge base). We propose to improve the distinction between these concepts, by revising a subset of the relations marked as false on the phenotype-gene relations corpus and give the first steps to automatically distinguish between the false (F), negative (N), and unknown (U) results. Our work resulted in a sample of 127 manually annotated FNU relations and a weighted-F1 of 0.5609 for their automatic distinction. This work was developed during the 6th Biomedical Linked Annotation Hackathon (BLAH6).

Semantic Relation Extraction using Pattern Pairs Sharing a Term (용어를 공유하는 패턴 쌍을 이용한 의미 관계 추출)

  • Kim, Se-Jong;Lee, Yong-Hun;Lee, Jong-Hyeok
    • Journal of KIISE:Computing Practices and Letters
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    • v.15 no.3
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    • pp.221-225
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    • 2009
  • Constructing an ontology using a mass corpus begins with an automatic semantic relation extraction. A general method regards words appearing between terms as patterns which are used to extract semantic relations. However, previous approaches consider only one sentence to extract a pattern, so they cannot extract semantic relations for terms in different sentences. This paper proposes a semantic relation extraction method using pairs of patterns sharing a term, where each pattern is extracted using one of the seed term pair satisfying the target relation. In our experiments, we achieved the accuracy 83.75% improving previous methods by 7.5% in is-${\alpha}$ relation and the accuracy 83.75% improved by 5% in part-of relation. We also present a possibility of improving the recall by the relative recall.

Web Image Caption Extraction using Positional Relation and Lexical Similarity (위치적 연관성과 어휘적 유사성을 이용한 웹 이미지 캡션 추출)

  • Lee, Hyoung-Gyu;Kim, Min-Jeong;Hong, Gum-Won;Rim, Hae-Chang
    • Journal of KIISE:Software and Applications
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    • v.36 no.4
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    • pp.335-345
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    • 2009
  • In this paper, we propose a new web image caption extraction method considering the positional relation between a caption and an image and the lexical similarity between a caption and the main text containing the caption. The positional relation between a caption and an image represents how the caption is located with respect to the distance and the direction of the corresponding image. The lexical similarity between a caption and the main text indicates how likely the main text generates the caption of the image. Compared with previous image caption extraction approaches which only utilize the independent features of image and captions, the proposed approach can improve caption extraction recall rate, precision rate and 28% F-measure by including additional features of positional relation and lexical similarity.

Acquisition of Named-Entity-Related Relations for Searching

  • Nguyen, Tri-Thanh;Shimazu, Akira
    • Proceedings of the Korean Society for Language and Information Conference
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    • pp.349-357
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    • 2007
  • Named entities (NEs) are important in many Natural Language Processing (NLP) applications, and discovering NE-related relations in texts may be beneficial for these applications. This paper proposes a method to extract the ISA relation between a "named entity" and its category, and an IS-RELATED-TO relation between the category and its related object. Based on the pattern extraction algorithm "Person Category Extraction" (PCE), we extend it for solving our problem. Our experiments on Wall Street Journal (WSJ) corpus show promising results. We also demonstrate a possible application of these relations by utilizing them for semantic search.

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