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TabEAno: Table to Knowledge Graph Entity Annotation [article]

Phuc Nguyen and Natthawut Kertkeidkachorn and Ryutaro Ichise and Hideaki Takeda
2020 arXiv   pre-print
To address these issues, we propose a novel approach, namely TabEAno, to semantically annotate table rows toward knowledge graph entities.  ...  Specifically, we introduce a "two-cells" lookup strategy bases on the assumption that there is an existing logical relation occurring in the knowledge graph between the two closed cells in the same row  ...  It means that the target knowledge graph (DBpedia) is completed and corrected. The table rows could only be matched when there is a corresponding entities in knowledge base.  ... 
arXiv:2010.01829v1 fatcat:nem3bp3znvdtdgjzadsput3hxu

Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service

Luca Foppiano, Laurent Romary
2020 Journal of the Japanese Association for Digital Humanities  
In this paper we aim at describing the functionalities of the service as a reference contribution to the subject of web-based NERD services.  ...  The representation is also compliant with the Web Annotation Data Model (WADM).  ...  The lookup attempts to find all mentions that correspond to either a title or an anchor (and variants thereof) in Wikipedia using an n-gram-based matching approach (with n = 6).  ... 
doi:10.17928/jjadh.5.1_22 fatcat:noxggjlhljbqbm3ibwm2p3tjiu

Multi-source named entity typing for social media

Reuth Vexler, Einat Minkov
2016 Proceedings of the Sixth Named Entity Workshop  
While lexicons may be derived from large-scale knowledge bases (KBs), KBs are inherently imperfect, in particular they lack coverage with respect to long tail entity names.  ...  We infer the types of a given entity name using multi-source learning, considering information obtained by alignment to the Freebase knowledge base, Web-scale distributional patterns, and global semi-structured  ...  Data: Entity name e, knowledge base k Result: S(e), a set of semantic types associated with e according to k initialization; 1.  ... 
doi:10.18653/v1/w16-2702 dblp:conf/aclnews/VexlerM16 fatcat:bbambkuanfbrrc7a3f3usnsiwi

Named Entity Recognition with Bidirectional LSTM-CNNs [article]

Jason P.C. Chiu, Eric Nichols
2016 arXiv   pre-print
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.  ...  We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches.  ...  The authors would like to thank Collobert et al. (2011b) for releasing SENNA with its word vectors and lexicon, the torch7 framework contributors, and Andrey Karpathy for the reference LSTM implementation  ... 
arXiv:1511.08308v5 fatcat:res6fbe7zvg6llf2axriltiq6y

Joint Entity Linking for Web Tables with Hybrid Semantic Matching [chapter]

Jie Xie, Yuhai Lu, Cong Cao, Zhenzhen Li, Yangyang Guan, Yanbing Liu
2020 Lecture Notes in Computer Science  
In order to extract the semantics of web tables to produce machine-readable knowledge, one of the critical steps is table entity linking, which maps the mentions in table cells to their referent entities  ...  in knowledge bases.  ...  For each mention in the web tables, we need to generate its candidate referent entities from a given knowledge base.  ... 
doi:10.1007/978-3-030-50417-5_46 fatcat:tluh7cwnibdptmh7zqkd4iytq4

Learning Cross-Context Entity Representations from Text [article]

Jeffrey Ling, Nicholas FitzGerald, Zifei Shan, Livio Baldini Soares, Thibault Févry, David Weiss, Tom Kwiatkowski
2020 arXiv   pre-print
Motivated by the observation that efforts to code world knowledge into machine readable knowledge bases or human readable encyclopedias tend to be entity-centric, we investigate the use of a fill-in-the-blank  ...  task to learn context independent representations of entities from the text contexts in which those entities were mentioned.  ...  TRAINING INPUT We do reduce the candidate set from the 5m entities covered by RELIC to the 818k entities in the TAC-KBP 2010 knowledge base to avoid ontological misalignment. table, RELIC matches  ... 
arXiv:2001.03765v1 fatcat:a5a37os4djgtxavz4rzne73zha

Named Entity Recognition with Bidirectional LSTM-CNNs

Jason P.C. Chiu, Eric Nichols
2016 Transactions of the Association for Computational Linguistics  
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.  ...  We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches.  ...  The authors would like to thank Collobert et al. (2011b) for releasing SENNA with its word vectors and lexicon, the torch7 framework contributors, and Andrey Karpathy for the reference LSTM implementation  ... 
doi:10.1162/tacl_a_00104 fatcat:j7xkgc4n3jdztoyackcgbxy2ne

Fine-Grained Named Entity Recognition using ELMo and Wikidata [article]

Cihan Dogan, Aimore Dutra, Adam Gara, Alfredo Gemma, Lei Shi, Michael Sigamani, Ella Walters
2019 arXiv   pre-print
Our work attempts to address these issues, in part, by combining state-of-the-art deep learning models (ELMo) with an expansive knowledge base (Wikidata).  ...  Using our framework, we cross-validate our model on the 112 fine-grained entity types based on the hierarchy given from the Wiki(gold) dataset.  ...  of text with reference to a knowledge base.  ... 
arXiv:1904.10503v1 fatcat:7sac2y6gmfbbhgt2znhkp3wzgq

Evaluating Named-Entity Recognition approaches in plant molecular biology [article]

Huy Do, Khoat Than, Pierre Larmande
2018 bioRxiv   pre-print
Text mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge.  ...  We applied Name Entities Recognition (NER) tagger, which is built from a Long Short Term Memory (LSTM) model, and combined with Conditional Random Fields (CRFs) to extract information of rice genes and  ...  We retrieved the word-level embedding from a lookup table of word embedding, meanwhile, we applied a bi-directional LSTM to the character sequence of each word and then concatenate both directions to achieve  ... 
doi:10.1101/360966 fatcat:2u4wcnuq7vctddsfdyh7yxsbbe

Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity [chapter]

David Nadeau, Peter D. Turney, Stan Matwin
2006 Lecture Notes in Computer Science  
We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands).  ...  We describe the system's architecture and compare its performance with a supervised system.  ...  Acknowledgements We would like to thank Caroline Barrière, who provided us with helpful comments on an earlier version of this work.  ... 
doi:10.1007/11766247_23 fatcat:rt67twcj3bbojcvgp5dxvszyrq

A Survey on Deep Learning for Named Entity Recognition [article]

Jing Li, Aixin Sun, Jianglei Han, Chenliang Li
2020 arXiv   pre-print
Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc.  ...  Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.  ...  Similar to [97] , character-level representation is concatenated with pre-trained word-level embedding from a word lookup table.  ... 
arXiv:1812.09449v3 fatcat:36tnstbyo5h4xizjpqn4cevgui

A Node Indexing Scheme for Web Entity Retrieval [chapter]

Renaud Delbru, Nickolai Toupikov, Michele Catasta, Giovanni Tummarello
2010 Lecture Notes in Computer Science  
In this paper, we present an "entity retrieval system" designed to provide entity search capabilities over datasets as large as the entire Web of Data.  ...  Now motivated also by the partial support of major search engines, hundreds of millions of documents are being published on the web embedding semi-structured data in RDF, RDFa and Microformats.  ...  The shift from documents to data entities poses new challenges for web search systems.  ... 
doi:10.1007/978-3-642-13489-0_17 fatcat:5jrfmfpcwvdrpozm2jl6oaesky

Robust Lexical Features for Improved Neural Network Named-Entity Recognition [article]

Abbas Ghaddar, Philippe Langlais
2018 arXiv   pre-print
We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia.  ...  Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features.  ...  We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. We thank the anonymous reviewers for their insightful comments.  ... 
arXiv:1806.03489v1 fatcat:qhq3mkmud5eapdz764inebudie

Identifying comparable entities on the web

Alpa Jain, Patrick Pantel
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
With this in mind, we present an initial step of mining comparable entities from sources of information available to a large-scale Web search engine, namely, search query logs and documents from a Web  ...  Web search engines are often presented with user queries that involve comparisons of real-world entities.  ...  from the Web.  ... 
doi:10.1145/1645953.1646198 dblp:conf/cikm/JainP09 fatcat:zyb5oysurvdhhbintsoninlfou

The Molecular Entities in Linked Data Dataset

Dominik Tomaszuk, Łukasz Szeremeta
2020 Data in Brief  
MEiLD contains 349,960 of 'small' chemical entities. Our dataset is based on the SDF files and is enriched with additional ontologies and line notation data.  ...  To describe chemical molecules, vocabularies such as Chemical Vocabulary for Molecular Entities (CVME) and Simple Knowledge Organization System (SKOS) are used.  ...  Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.dib.2020.105757 .  ... 
doi:10.1016/j.dib.2020.105757 pmid:32529012 pmcid:PMC7276506 fatcat:d6nqo3gww5gzxho7dym5t4k6ea
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