Relation Extraction 2015


Notes:

A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents.

  • Automatic relation extraction

Resources:

Wikipedia:

See also:

100 Best GitHub: Relation Extraction | 100 Best Relation Extraction VideosRelation Extraction & Dialog SystemsRelation Extraction & Question Answering Systems 2014Relation Extraction 2014


Injecting logical background knowledge into embeddings for relation extraction T Rocktäschel, S Singh… – Proceedings of the 2015 …, 2015 – anthology.aclweb.org Abstract Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortunately, these methods share a shortcoming with all other distantly supervised Cited by 30 Related articles All 10 versions

Construction of semantic bootstrapping models for relation extraction C Zhang, W Xu, Z Ma, S Gao, Q Li, J Guo – Knowledge-Based Systems, 2015 – Elsevier Abstract Traditionally, pattern-based relation extraction methods are usually based on iterative bootstrapping model which generally implies semantic drift or low recall problem. In this paper, we present a novel semantic bootstrapping framework that uses semantic Cited by 8 Related articles All 3 versions

Distant supervision for relation extraction via piecewise convolutional neural networks D Zeng, K Liu, Y Chen, J Zhao – Proceedings of the 2015 …, 2015 – emnlp2015.org Abstract Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, Cited by 19 Related articles All 9 versions

Relation extraction: Perspective from convolutional neural networks TH Nguyen, R Grishman – Proceedings of NAACL-HLT, 2015 – pdfs.semanticscholar.org Abstract Up to now, relation extraction systems have made extensive use of features generated by linguistic analysis modules. Errors in these features lead to errors of relation detection and classification. In this work, we depart from these traditional approaches with Cited by 20 Related articles All 9 versions

Improved relation extraction with feature-rich compositional embedding models MR Gormley, M Yu, M Dredze – arXiv preprint arXiv:1505.02419, 2015 – arxiv.org Abstract: Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, Cited by 14 Related articles All 12 versions

PKDE4J: Entity and relation extraction for public knowledge discovery M Song, WC Kim, D Lee, GE Heo, KY Kang – Journal of biomedical …, 2015 – Elsevier Abstract Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of Cited by 13 Related articles All 5 versions

RELigator: chemical-disease relation extraction using prior knowledge and textual information E Pons, BFH Becker, SA Akhondi, Z Afzal… – Proceedings of the …, 2015 – biocreative.org Abstract. The Erasmus MC team participated in the chemical-disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtask: automatic disease named entity recognition and normalization (DNER) and extraction of chemical-induced Cited by 6 Related articles All 2 versions

Multilingual open relation extraction using cross-lingual projection M Faruqui, S Kumar – arXiv preprint arXiv:1503.06450, 2015 – arxiv.org Abstract: Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema. However, current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on Cited by 7 Related articles All 11 versions

Combining word embeddings and feature embeddings for fine-grained relation extraction M Yu, MR Gormley, M Dredze – … American Chapter of the Association for …, 2015 – aclweb.org Abstract Compositional embedding models build a representation for a linguistic structure based on its component word embeddings. While recent work has combined these word embeddings with hand crafted features for improved performance, it was restricted to a small Cited by 11 Related articles All 10 versions

Multilingual relation extraction using compositional universal schema P Verga, D Belanger, E Strubell, B Roth… – arXiv preprint arXiv: …, 2015 – arxiv.org Abstract: When building a knowledge base (KB) of entities and relations from multiple structured KBs and text, universal schema represents the union of all input schema, by jointly embedding all relation types from input KBs as well as textual patterns expressing Cited by 8 Related articles All 6 versions

Adapting a rule-based relation extraction system for BioCreative V BEL task RK Elayavilli, M Rastegar-Mojarad… – the Fifth BioCreative …, 2015 – researchgate.net We tested a rule-based semantic parser in the BEL statement extraction task of BioCreative V Track4 challenge. While the system achieved an overall F-measure of 21.29% with gold standard entities, it achieved a very low performance of 13.86% with the entities extracted by Cited by 5 Related articles All 2 versions

Improvement of n-ary relation extraction by adding lexical semantics to distant-supervision rule learning H Li, S Krause, F Xu, A Moro, H Uszkoreit… – Proc. of ICAART, …, 2015 – Citeseer Abstract: A new method is proposed and evaluated that improves distantly supervised learning of pattern rules for n-ary relation extraction. The new method employs knowledge from a large lexical semantic repository to guide the discovery of patterns in parsed relation Cited by 4 Related articles All 6 versions

An extended dependency graph for relation extraction in biomedical texts Y Peng, S Gupta, CH Wu, K Vijay-Shanker – ACL-IJCNLP 2015, 2015 – aclweb.org Abstract Kernel-based methods are widely used for relation extraction task and obtain good results by leveraging lexical and syntactic information. However, in biomedical domain these methods are limited by the size of dataset and have difficulty in coping with variations in text. Cited by 6 Related articles All 7 versions

Semantic representations for domain adaptation: a case study on the tree kernel-based method for relation extraction TH Nguyen, B Plank, R Grishman – … of the 53rd Annual Meeting of the …, 2015 – aclweb.org Abstract We study the application of word embeddings to generate semantic representations for the domain adaptation problem of relation extraction (RE) in the tree kernelbased method. We systematically evaluate various techniques to generate the semantic Cited by 11 Related articles All 11 versions

Wide-coverage relation extraction from MEDLINE using deep syntax NTH Nguyen, M Miwa… – BMC …, 2015 – bmcbioinformatics.biomedcentral. … Background Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted Cited by 3 Related articles All 15 versions

Exploring pattern structures of syntactic trees for relation extraction A Leeuwenberg, A Buzmakov, Y Toussaint… – … Conference on Formal …, 2015 – Springer Abstract In this paper we explore the possibility of defining an original pattern structure for managing syntactic trees. More precisely, we are interested in the extraction of relations such as drug-drug interactions (DDIs) in medical texts where sentences are represented as Cited by 4 Related articles All 4 versions

Chemical-induced disease relation extraction with lexical features JH Gu, LH Qian, GD Zhou – Proceedings of the fifth BioCreative …, 2015 – biocreative.org Abstract. This paper briefly describes our basic work on the chemical-induced disease relation extraction task on BioCreative-V Track-3b. It is a machine learning based system which utilizes simple yet effective lexical features. Pairs of chemical and disease mentions Cited by 3 Related articles

Towards a relation extraction framework for cyber-security concepts CL Jones, RA Bridges, KMT Huffer… – Proceedings of the 10th …, 2015 – dl.acm.org Abstract In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we Cited by 4 Related articles All 7 versions

Relation extraction from community generated question-answer pairs D Savenkov, WL Lu, J Dalton, E Agichtein – NAACL-HLT 2015 Student …, 2015 – aclweb.org Abstract Community question answering (CQA) websites contain millions of question and answer (QnA) pairs that represent real users’ interests. Traditional methods for relation extraction from natural language text operate over individual sentences. However answer Cited by 3 Related articles All 10 versions

Analysing the Role of Representation Choices in Portuguese Relation Extraction S Collovini, P Marcelo de Bairros Filho… – … Conference of the Cross- …, 2015 – Springer Abstract Relation Extraction is the task of identifying and classifying the semantic relations between entities in text. This task is one of the main challenges in Natural Language Processing. In this work, the relation extraction task is treated as sequence labelling Cited by 3 Related articles

Relation extraction pattern ranking using word similarity K Lambrou-Latreille – NAACL-HLT 2015 Student Research …, 2015 – anthology.aclweb.org Abstract Our thesis proposal aims at integrating word similarity measures in pattern ranking for relation extraction bootstrapping algorithms. We note that although many contributions have been done on pattern ranking schemas, few explored the use of word-level semantic Cited by 2 Related articles All 7 versions

Combining Neural Networks and Log-linear Models to Improve Relation Extraction TH Nguyen, R Grishman – arXiv preprint arXiv:1511.05926, 2015 – arxiv.org Abstract: The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks has provided very effective Cited by 7 Related articles All 3 versions

Mining activation force defined dependency patterns for relation extraction C Zhang, Y Zhang, W Xu, Z Ma, Y Leng… – Knowledge-Based Systems, 2015 – Elsevier Abstract Relation extraction is essential for most text mining tasks. Existing approaches on relation extraction are generally based on bootstrapping methodology which implies semantic drift problem. This paper presents a new approach to learn semantic dependency Cited by 3 Related articles All 4 versions

Core: Context-aware open relation extraction with factorization machines F Petroni, L Del Corro, R Gemulla – Proceedings of EMNLP, 2015 – anthology.aclweb.org Abstract We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information Cited by 3 Related articles All 12 versions

Towards efficient support relation extraction from RGBD images F Xue, S Xu, C He, M Wang, R Hong – Information Sciences, 2015 – Elsevier Abstract To extract reasonable support relations from “RGB+ depth”(RGBD) images, it is very important to achieve good scene understanding. This paper proposes a novel approach to extracting accurate support relationships by analyzing the RGBD images of indoor scenes. Cited by 4 Related articles All 3 versions

Sieve-based relation extraction of gene regulatory networks from biological literature S Žitnik, M Žitnik, B Zupan… – BMC …, 2015 – bmcbioinformatics.biomedcentral. … Background Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions Cited by 3 Related articles All 10 versions

Exploring the effectiveness of linguistic knowledge for biographical relation extraction M Garcia, P Gamallo – Natural Language Engineering, 2015 – Cambridge Univ Press Abstract Machine learning techniques have been implemented to extract instances of semantic relations using diverse features based on linguistic knowledge, such as tokens, lemmas, PoS-tags, or dependency paths. However, there has been little work aiming to Cited by 4 Related articles All 3 versions

HITSZ_CDR System for Disease and Chemical Named Entity Recognition and Relation Extraction H Li, Q Chen, K Chen, B Tang – Proceedings of the Fifth BioCreative …, 2015 – biocreative.org Abstract. In this paper, an end-to-end machine learning-based system was proposed for the challenge task of chemical and disease named entity recognition (DNER) and chemical- induced diseases (CID) relation extraction in BioCreative V, where DNER includes chemical Cited by 1 Related articles

Distant supervision for relation extraction using tree kernels A Abad, A Moschitti – 2015 – Citeseer Abstract. In this paper we define a simple Relation Extraction system based on SVMs using tree kernels and employing a weakly supervised approach, known as Distant Supervision (DS). Our method uses the simple one-versus-all strategy to handle overlapping relations, Cited by 1 Related articles All 7 versions

Distant Supervision for Relation Extraction via Group Selection Y Xiang, X Wang, Y Zhang, Y Qin, S Fan – International Conference on …, 2015 – Springer Abstract Distant supervision (DS) aligns relations between name entities from a knowledge base (KB) with free text and automatically annotates the training corpus with relation mentions. One big challenge of DS is that the heuristically generated relation labels usually Cited by 1 Related articles

Held-out versus Gold Standard: Comparison of Evaluation Strategies for Distantly Supervised Relation Extraction from Medline abstracts RA Roller, M Stevenson – Proceedings of the Sixth …, 2015 – eprints.whiterose.ac.uk Distant supervision is a useful technique for creating relation classifiers in the absence of labelled data. The approaches are often evaluated using a held-out portion of the distantly labelled data, thereby avoiding the need for lablelled data entirely. However, held-out Cited by 3 Related articles All 14 versions

Aspect-Based Sentiment Analysis Using Tree Kernel Based Relation Extraction TH Nguyen, K Shirai – … Conference on Intelligent Text Processing and …, 2015 – Springer Abstract We present an application of kernel methods for extracting relation between an aspect of an entity and an opinion word from text. Two tree kernels based on the constituent tree and dependency tree were applied for aspect-opinion relation extraction. In addition, we Cited by 3 Related articles All 3 versions

UTD: Ensemble-based spatial relation extraction J D’Souza, V Ng – Proceedings of the 9th International Workshop on …, 2015 – aclweb.org Abstract SpaceEval (SemEval 2015 Task 8), which concerns spatial information extraction, builds on the spatial role identification tasks introduced in SemEval 2012 and used in SemEval 2013. Among the host of subtasks presented in SpaceEval, we participated in Cited by 2 Related articles All 10 versions

Effectiveness of Keyword and Semantic Relation Extraction for Knowledge Map Generation V Sornlertlamvanich, C Kruengkrai – International Workshop on Worldwide …, 2015 – Springer Abstract We explore the named entity (NE) recognition and semantic relation extraction technique on the Thai cultural database. Within the limited domain and well-structured database, our proposed method can perform in an acceptable high accuracy to generate the Cited by 1 Related articles All 6 versions

Comparison between Surface-based and Dependency-based Relation Extraction Approaches for Automatic Generation of Multiple-Choice Questions N Afzal, A Bawakid – 2015 – ijmse.org Abstract—Multiple Choice Questions (MCQs) are frequently used as an assessment tool in various e-Learning applications. In this paper, we compare two systems for automatic generation of multiple-choice question (MCQs) that are based on semantic relations. Both Cited by 1 Related articles All 2 versions

Leveraging Chinese Encyclopedia for Weakly Supervised Relation Extraction X Guo, T He – Joint International Semantic Technology Conference, 2015 – Springer Abstract In the research of named-entity relation extraction based on supervision, selecting relation features for traditional methods are usually finished by people, and it’s hard to implement these methods for large-scale corpus. On the other hand, fixing relation types is Cited by 1 Related articles All 3 versions

Towards combined matrix and tensor factorization for universal schema relation extraction S Singh, T Rocktäschel, S Riedel – Proceedings of NAACL-HLT, 2015 – aclweb.org Abstract Matrix factorization of knowledge bases in universal schema has facilitated accurate distantlysupervised relation extraction. This factorization encodes dependencies between textual patterns and structured relations using lowdimensional vectors defined for Cited by 1 Related articles All 11 versions

Optimizing multivariate performance measures for learning relation extraction models G Haffari, A Nagesh, G Ramakrishnan – … of the North American Chapter of …, 2015 – aclweb.org Abstract We describe a novel max-margin learning approach to optimize non-linear performance measures for distantly-supervised relation extraction models. Our approach can be generally used to learn latent variable models under multivariate non-linear Cited by 2 Related articles All 6 versions

A distributed meta-learning system for Chinese entity relation extraction L Li, J Zhang, L Jin, R Guo, D Huang – Neurocomputing, 2015 – Elsevier Abstract Entity relation extraction is an important task for obtaining useful information from multiple text documents. This paper presents a distributed meta-learning method which incorporates the distributed system and the meta-learning strategy for Chinese entity relation Cited by 1 Related articles All 3 versions

Bias Modeling for Distantly Supervised Relation Extraction Y Xiang, Y Zhang, X Wang, Y Qin… – Mathematical …, 2015 – downloads.hindawi.com 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 Cited by 1 Related articles All 8 versions

Distantly supervised web relation extraction for knowledge base population I Augenstein, D Maynard, F Ciravegna – Semantic Web, 2015 – content.iospress.com Abstract Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information Cited by 3 Related articles All 7 versions

INDREX: In-database relation extraction T Kilias, A Löser, P Andritsos – Information Systems, 2015 – Elsevier Abstract The management of text data has a long-standing history in the human mankind. A particular common task is extracting relations from text. Typically, the user performs this task with two separate systems, a relation extraction system and an SQL-based query engine for Cited by 2 Related articles All 5 versions

Evaluation of methods for taxonomic relation extraction from text RL Granada – 2015 – meriva.pucrs.br Sistemas de informação modernos têm mudado a ideia “processamento de dados” para a ideia de “processamento de conceitos”, assim, ao invés de processarem palavras, tais sistemas fazem o processamento de conceitos que contêm ignificado e que compartilham Cited by 1

Extreme extraction: Only one hour per relation R Hoffmann, L Zettlemoyer, DS Weld – arXiv preprint arXiv:1506.06418, 2015 – arxiv.org … First, they have not been evaluated on relation extraction tasks. Second, and more importantly, their general approach is to consider a particu- lar type of feedback and then develop algorithms for learning more accurately from such feedback. … Cited by 1 Related articles All 3 versions

A bootstrapping algorithm for geo-entity relation extraction from online encyclopedia L Yu, F Lu – Geoinformatics, 2015 23rd International …, 2015 – ieeexplore.ieee.org Abstract-Extracting spatial and semantic relations between two geo-entities from web texts, is one core problem of geographical information retrieval. The primary methods are pattern matching and supervised learning. Since the coverage of patterns is limited due to poor Related articles

A Hybrid Framework for Semantic Relation Extraction over Enterprise Data W Shen, J Wang, P Luo, M Wang – International Journal on Semantic …, 2015 – igi-global.com Abstract Relation extraction from the Web data has attracted a lot of attention in recent years. However, little work has been done when it comes to relation extraction from the enterprise data regardless of the urgent needs to such work in real applications (eg, E-discovery). One Cited by 1 Related articles All 2 versions

Constraint-based metric-aware approach for relation co-extraction X Chen – 2015 – ideals.illinois.edu … Abstract: This thesis focuses on relation extraction within unstructured text data. We are interested in the bootstrapping approach, in which only a small portion of examples are given to train the extractor. The training of the extractor …

Relation Extraction for Matrices (type) entities H Shukla, K Gaurav – 2015 – cse.iitk.ac.in … Relation Extraction for Matrices(type) entities in Introductory programing problems Himanshu Shukla(13309)1 Kumar Gaurav(12368)1 1Computer Science and Engineering Department IIT Kanpur 31 March, 2015 Himanshu Shukla(13309) Kumar Gaurav(12368) IIT Related articles All 3 versions

A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns L Hennig, H Li, S Krause, F Xu… – ACL-IJCNLP …, 2015 – anthology.aclweb.org Abstract Patterns extracted from dependency parses of sentences are a major source of knowledge for most state-of-the-art relation extraction systems, but can be of low quality in distantly supervised settings. We present a linguistic annotation tool that allows human Related articles All 8 versions

Enhancement of Binary Text Classification Using Automatic Relation Extraction A Nugumanova, Y Baiburin, I Bessmertny – Creativity in Intelligent, …, 2015 – Springer Abstract The bag-of-words model is the most common text representation model used in information retrieval and text classification. It represents a text as a bag of words without regard to word order and relationship. We propose a new text classification approach that is Related articles

A Flexible Text Mining System for Entity and Relation Extraction in PubMed GE Heo, KY Kang, M Song – Proceedings of the ACM Ninth International …, 2015 – dl.acm.org Abstract Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of

Using Entity Information from a Knowledge Base to Improve Relation Extraction L Du, A Kumar, M Johnson… – Presented at ALTA2015 …, 2015 – science.mq.edu.au Page 1. Using Entity Information from a Knowledge Base to Improve Relation Extraction Lan Du1, Anish Kumar2, Mark Johnson2 and Massimiliano Ciaramita3 1Faculty of Information Technology, Monash University, Australia 2Department of Computing, Macquarie University, Related articles All 12 versions

Relation Extraction K Denecke – Health Web Science, 2015 – Springer Abstract Relationships semantically connect entities and thus it is crucial to identify them when analysing texts in order to understand and interpret the content correctly. Only with extracted relations, a deeper text understanding (eg, recognising the who, when, where of a Related articles

Relation Extraction for Matrix (type) entities in Introductory programing problems H Shukla, K Gaurav – 2015 – cse.iitk.ac.in Abstract Relation Extraction has been an important task in natural language processing since early 1990s and there is no need to specify the use of relation extraction in real life. Relation extraction has been done in lot of fields for example bioinformatics, organisation- Related articles All 6 versions

Grammatical case based IS-A relation extraction with boosting ICS PAS – konferencja.osaweb.pl Relation extraction from natural language text is a necessary step of any ontology induction or taxonomy induction task. Typically it takes as input morpho-syntactically annotated text and produces a set of triples (E1, R, E2), where E1 and E2 are entities and R is a relation in Related articles All 2 versions

Open Relation Extraction for Polish: Preliminary Experiments J Piskorski – BSNLP 2015 – anthology.aclweb.org Abstract This paper presents preliminary experiments on Open Relation Extraction for Polish. In particular, a variant of a priorart algorithm for open relation extraction for English has been adapted and tested on a set of articles from Polish on-line news. The paper All 9 versions

Incremental construction of biological networks by relation extraction from literature D Miljkovic, V Podpe?an, T Stare, I Mozetic… – Current …, 2015 – ingentaconnect.com This work focuses on automated incremental development of biological networks. The Bio3graph approach to information extraction from biological literature is extended with new features which allow for periodical updates of network structures using newly published Related articles All 7 versions

Optimizing Multivariate Performance Measures for Learning Relation Extraction Models G Ramakrishnan, G Haffari, A Nagesh – 2015 – cse.iitb.ac.in Page 1. Optimizing Multivariate Performance Measures for Learning Relation Extraction Models Optimizing Multivariate Performance Measures for Learning Relation Extraction Models … Experiments Conclusion 3/37 Page 4. Optimizing Multivariate Performance Measures for Related articles

Exposing ambiguities in a relation-extraction gold standard with crowdsourcing TS Li, BM Good, AI Su – arXiv preprint arXiv:1505.06256, 2015 – arxiv.org Abstract: Semantic relation extraction is one of the frontiers of biomedical natural language processing research. Gold standards are key tools for advancing this research. It is challenging to generate these standards because of the high cost of expert time and the Cited by 1 Related articles All 3 versions

Syntactic and semantic structures for relation extraction DT Vo, E Bagheri – Proceedings of the 6th Symposium on Future …, 2015 – dl.acm.org Abstract This study proposes to employ syntactic and semantic knowledge from the rich relations within a tree kernel structure for relation extraction. The underlying idea is that different tree kernels with a variety of representations of the available linguistic information Related articles

DUTIR at the BioCreative V CDR Task: Disease Named Entity Recognition and Normalization and the Chemical-Disease Relation Extraction from Biomedical Text Z Li, ZY YaYang, Z Zhou, H Lin – biocreative.org Abstract. Adverse drug reactions between chemicals and diseases make the topic of chemical-disease relations (CDR) become a focus that receives much concern. In this paper, we introduce our methods used to create our submissions to the BioCreative V CDR Related articles

Handling uncertainty in relation extraction: a case study on tennis tournament results extraction from tweets JGJ Verburg, MB Habib, M van Keulen – Proceedings of the 8th …, 2015 – dl.acm.org Abstract Relation extraction involves different types of uncertainty due to the imperfection of the extraction tools and the inherent ambiguity of unstructured text. In this paper, we discuss several ways of handling uncertainties in relation extraction from social media. Our study Related articles All 5 versions

REMed: automatic relation extraction from medical documents M Porumb, I Barbantan, C Lemnaru… – Proceedings of the 17th …, 2015 – dl.acm.org Abstract The large amount of unstructured medical documents written in natural language bears a massive quantity of knowledge, whose extraction becomes useful. An automatic relation identification strategy leads to the discovery of relations,(possible unknown) Cited by 2 Related articles

Erratum: Aspect-Based Sentiment Analysis Using Tree Kernel Based Relation Extraction TH Nguyen, K Shirai – … Conference on Intelligent Text Processing and …, 2015 – Springer Abstract In the originally published version, the 12 th reference was wrong. It should read as follows: 12. Nguyen, TH, Shirai, K.: Text classification of technical papers based on text segmentation. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S.(eds.) NLDB

Domain-specific Relation Extraction: using distant supervision Machine Learning A Aljamel, T Osman, G Acampora – … Management (IC3K), 2015 …, 2015 – ieeexplore.ieee.org Abstract: The increasing accessibility and availability of online data provides a valuable knowledge source for information analysis and decision-making processes. In this paper we argue that extracting information from this data is better guided by domain knowledge of the

Artificial bee colony-based extraction of non-taxonomic relation between symptom and syndrome in TCM records F Yuan, S Chen, H Liu, L Xu – International Journal of …, 2015 – inderscienceonline.com … By combing the vast merging/evolution diversity of niche technique and rapid non-taxonomic relation extraction of ABC algorithm, the proposed algorithm was able to resolve the problems of local optimum and rules redundancy. … Cited by 2 Related articles All 2 versions

miRTex: A Text Mining System for miRNA-Gene Relation Extraction G Li, KE Ross, CN Arighi, Y Peng, CH Wu… – PLoS Comput …, 2015 – journals.plos.org Abstract MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe Cited by 5 Related articles All 13 versions

ArabRelat: Arabic Relation Extraction using Distant Supervision R Mohamed, NM El-Makky, K Nagi – researchgate.net Abstract: Relation Extraction is an important preprocessing task for a number of text mining applications, including: Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a novel Arabic relation extraction method that Related articles

A Benchmark for Relation Extraction Kernels JLM Pereira, H Galhardas, B Martins – East European Conference on …, 2015 – Springer Abstract Relation extraction from textual documents is an important task in the context of information extraction. This task aims at identifying relations between pairs of named entities and assigning them a type. Relation extraction is often approached as a supervised Related articles

N-ary Relation Extraction for Joint T-Box and A-Box Knowledge Base Augmentation M Fossati, E Dorigatti, C Giuliano – semantic-web-journal.net Abstract. The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one still being the free-text document. This motivates the need for Intelligent Web-reading Agents: hypothetically, they would skim through disparate Web sources Related articles

Competition Relation Extraction based on Combining Machine Learning and Filtering CH Lee, YH Seo, HK Kim – Journal of KIISE, 2015 – koreascience.or.kr Abstract This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new

Relation Extraction from Wikipedia Leveraging Intrinsic Patterns Y Gu, W Liu, J Song – … on Web Intelligence and Intelligent Agent …, 2015 – ieeexplore.ieee.org Abstract—Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it’s Cited by 1 Related articles All 2 versions

Using Distant Supervision and Paragraph Vector for Large Scale Relation Extraction Y Liu, W Xu – National Conference on Big Data Technology and …, 2015 – Springer Abstract Distant supervision has the ability to generate a huge amount training data. Recently, the multi-instance multi-label learning is imported to distant supervision to combat noisy data and improve the performance of relation extraction. But multi-instance multi-label Related articles

Relation extraction based on two-step classification with distant supervision M Choi, H Lee, H Kim – The Journal of Supercomputing, 2015 – Springer Abstract Supervised machine learning methods have been widely used in relation extraction to find the relation between two named entities in a sentence. However, the disadvantages of supervised machine learning methods are that constructing the training data set is costly Related articles

Improving Relation Extraction by Using an Ontology Class Hierarchy Feature PHR Assis, MA Casanova, AHF Laender… – … Conference on Web …, 2015 – Springer Abstract Relation extraction is a key step to address the problem of structuring natural language text. This paper proposes a new ontology class hierarchy feature to improve relation extraction when applying a method based on the distant supervision approach. It Cited by 1 Related articles All 3 versions

SCHN APPER: A Web Toolkit for Exploratory Relation Extraction T Michael, A Akbik – ACL-IJCNLP 2015, 2015 – anthology.aclweb.org Abstract We present SCHN APPER, a web toolkit for Exploratory Relation Extraction (ERE). The tool allows users to identify relations of interest in a very large text corpus in an exploratory and highly interactive fashion. With this tool, we demonstrate the easeof-use and Cited by 2 Related articles All 7 versions

Concept relation extraction using Naïve Bayes classifier for ontology-based question answering systems G Zayaraz – Journal of King Saud University-Computer and …, 2015 – Elsevier Abstract Domain ontology is used as a reliable source of knowledge in information retrieval systems such as question answering systems. Automatic ontology construction is possible by extracting concept relations from unstructured large-scale text. In this paper, we propose Cited by 2 Related articles All 3 versions

A method based on word feature selection for relation extraction in EMRs XB Lv, Y Guan, JW Wu – … 2015 (ICEEIS 2015), January 17-18, …, 2015 – books.google.com ABSTRACT: Relations between entities in electronic medical records play an important role in understanding the content of EMRs. A CRF-based machine learning method was applied in this paper. In order to select a better feature set, we first merged the jargon words and Related articles

Domain specific commonsense relation extraction from bag of concepts metadata J Li – Proceedings of the 9th International Conference on …, 2015 – dl.acm.org Abstract Existing semantic knowledge bases such as WordNet and Yago contain the information of relations between entities. They do not hold the information about domain specific commonsense relations between concepts like” horse” and” farm” which intuitively Related articles

Online Inference for Relation Extraction with a Reduced Feature Set M Rabinovich, C Archambeau – arXiv preprint arXiv:1504.04770, 2015 – arxiv.org Abstract: Access to web-scale corpora is gradually bringing robust automatic knowledge base creation and extension within reach. To exploit these large unannotated—and extremely difficult to annotate—corpora, unsupervised machine learning methods are Related articles All 6 versions

Relation Extraction Using Semantic Information J Xu, Q Lu, M Li – International Conference of the Pacific Association for …, 2015 – Springer Abstract Research works on relation extraction have put a lot of attention on finding features of surface text and syntactic patterns between entities. Much less work is done using semantically relevant features between entities because semantic information is difficult to Related articles All 3 versions

Relation extraction from Chinese online encyclopedia based on weakly supervised learnin Z JIA, D HE, Y YANG, Y YANG, Z YE – CAAI Transactions on …, 2015 – en.cnki.com.cn Entity relation extraction plays an important role in the fields of information retrieval, automatic question answering and ontology learning. An entity relation extraction frame based on weakly-supervised learning is proposed in the paper. First, training data are

A Rule-based Methodology and Feature-based Methodology for Effect Relation Extraction in Chinese Unstructured Text J Wang – 2015 – ses.library.usyd.edu.au The Chinese language differs significantly from English, both in lexical representation and grammatical structure. These differences lead to problems in the Chinese NLP, such as word segmentation and flexible syntactic structure. Many conventional methods and Related articles All 2 versions

Extraction of Uyghur Comparative Relation? S Tiana, H Wangb, L Yuc, T Ibrahimb, A Hamdullaa – joics.com … The experimental results show the efficiency of proposed method in comparative relation extraction task. Keywords: Uyghur; Comparative Relation Extraction; Comparative Entity 1 Introduction … 3 Comparative Relation Extraction We divide our work into three tasks. … Related articles

Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning D Jang, J Lee, K Kim, D Lee – … of the ACM Ninth International Workshop …, 2015 – dl.acm.org Abstract The scientific literature is a rich resource for information retrieval on the biological knowledge. Nevertheless, the unstructured textual data in the research articles makes it difficult to access the information with computer-aided systems. Text-mining is one of the Related articles

Chinese Relation Extraction by Multiple Instance Learning YJ Chen, JY Hsu – 2015 – csie.ntu.edu.tw Abstract Relation extraction, which learns semantic relations of concept pairs from text is an approach for mining commonsense knowledge. This paper investigates an approach for relation extraction, which helps expand a commonsense knowledge base with little labor Related articles All 2 versions

On Efficiency of Semantic Relation Extraction through Low-dimensional Distributed Representations for Substrings Z Jin, C Shibata, J Sun, K Tago – High Performance Computing …, 2015 – ieeexplore.ieee.org Abstract—By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from big data. To analyze big data, efficient and smart representations of data achieved by using sufficiently fast algorithms, as well as highly Related articles All 2 versions

A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set AW Muzaffar, F Azam, U Qamar – Computational and mathematical …, 2015 – hindawi.com The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, Cited by 5 Related articles All 8 versions

Relation dictionary construction and rule learning for PPI extraction from biomedical literatures X Guo, T He, J Yuan – Bioinformatics and Biomedicine (BIBM), …, 2015 – ieeexplore.ieee.org … Liu et al. [18] developed a distant learning approach that incorporated the results from existing open information extraction techniques to perform relation extraction task in biomedical domain without using any hand labeled examples. Andrew Carlson et al. … Related articles All 2 versions

Biomedical relation extraction using stochastic difference equations CT Fakhry, K Zarringhalam… – High Performance Extreme …, 2015 – ieeexplore.ieee.org Abstract—We propose an unsupervised method for extracting causal relations between biomedical entities using stochastic difference equations (SDE). Our method attempts to generalize the propagation of relevance in medical sentences in order to extract related Related articles

Automatic relation extraction using naïve Bayes classifier for concept relational ontology development G Sureshkumar, G Zayaraz – International Journal of …, 2015 – inderscienceonline.com In this paper we proposed a methodology to learn concept relations from the unstructured text using dependency parsing pattern-based hand coded rules and to automatically construct domain ontology using extracted concept relations. The pattern mining was Related articles

Integrating Multiple On-line Knowledge Bases for Disease-Lab Test Relation Extraction Y Zhang, E Soysal, S Moon, J Wang… – AMIA Summits on …, 2015 – ncbi.nlm.nih.gov Abstract A computable knowledge base containing relations between diseases and lab tests would be a great resource for many biomedical informatics applications. This paper describes our initial step towards establishing a comprehensive knowledge base of disease Cited by 1 Related articles All 4 versions

A Comparative Study on Arabic Grammatical Relation Extraction Based on Machine Learning Classification MA Falih, N Omar – Middle-East Journal of Scientific Research, 2015 – idosi.org Abstract: Grammatical Relation (GR) can be defined as a linguistic relation established by grammar, in which the linguistic relation is an association between linguistic forms or constituents. Fundamentally, GRs determine grammatical behavior, such as the placement Cited by 1 All 2 versions

Distantly Supervised Neural Network Model for Relation Extraction Z Wang, B Chang, Z Sui – … and Natural Language Processing Based on …, 2015 – Springer Abstract For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base (KB) with free texts. Albeit easy to scale to thousands of different relations, this procedure suffers from introducing wrong labels Related articles All 2 versions

Sieve-Based Spatial Relation Extraction with Expanding Parse Trees J D’Souza, V Ng – aclweb.org Abstract A key challenge introduced by the recent SpaceEval shared task on spatial relation extraction is the identification of MOVELINKs, a type of spatial relation in which up to eight spatial elements can participate. To handle the complexity of extracting MOVELINKs, we Related articles All 9 versions

Fast and Large-scale Unsupervised Relation Extraction S Takase, N Okazaki, K Inui – 2015 – bcmi.sjtu.edu.cn Abstract A common approach to unsupervised relation extraction builds clusters of patterns expressing the same relation. In order to obtain clusters of relational patterns of good quality, we have two major challenges: the semantic representation of relational patterns and the Cited by 1 Related articles All 8 versions

Relation Extraction from Texts with Symbolic Rules Induced by Inductive Logic Programming R Lima, B Espinasse, F Freitas – Tools with Artificial Intelligence …, 2015 – ieeexplore.ieee.org Abstract—Relation Extraction (RE) is the task of detecting semantic relations between entities in text. Most of the state-ofthe-art RE systems rely on statistical machine learning techniques which usually employ an attribute-value representation of features. Contrarily to Related articles All 2 versions

Named Entity Relation Extraction Based on Multiple Features Y Li – … and Applications Workshops (WAINA), 2015 IEEE 29th …, 2015 – ieeexplore.ieee.org Abstract—For the limited availability of established Mongolian named entity dictionary, with the increase of the new terminology and network vocabulary, Mongolian named entity dictionary was not able to update in time. This paper puts forward a method of named entity Cited by 1 Related articles All 3 versions

A Context-Aware Relation Extraction Method for Relation Completion B Sivaranjani, M Selvaraj – pdfs.semanticscholar.org ABSTRACT Identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation R, RC attempts at linking entity pairs between two entity lists under

Interactive Learning with TREE: Teachable Relation and Event Extraction System M Tydykov, M Zeng, A Gershman… – … on Applications of Natural …, 2015 – Springer … Abstract. Information extraction, and specifically event and relation extraction from text, is an important problem in the age of big data. Current solutions to these problems require large amounts of training data or extensive feature engineering to find domain-specific events. … Related articles

N-ary Relation Approach for Open Domain Question Answering System Based on Information Extraction through World Wide Web R Yadav, SR Tandan – ijeas.org … (Thomas Lin et al., 2010) described information extraction as common sense and are denoted by f (a, b) where f is relation between attribute a and b. They have employed Open Information Extraction (Open IE) for relation extraction. … Related articles