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The TALP participation at ERD 2014

Ali M. Naderi, Horacio Rodriguez, Jordi Turmo
2014 Proceedings of the first international workshop on Entity recognition & disambiguation - ERD '14  
To this end, we presented our system taking advantage of a topic modeling approach to rank candidates of each entity mentions occurring in the query text.  ...  The objective of this evaluation track is to recognize mentions of entities in a given short text, disambiguate them and map them to the entities in a given collection of knowledge base.  ...  In the short text track, given a query text for each query, the ERD system should use all available context to select the correct KB candidate of each entity surface form occurring in the query text.  ... 
doi:10.1145/2633211.2634359 dblp:conf/sigir/NaderiRT14 fatcat:hkuzcrozmfetxcvxzeh3v3rzya

Entity Disambiguation with Web Links

Andrew Chisholm, Ben Hachey
2015 Transactions of the Association for Computational Linguistics  
We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia.  ...  Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.  ...  Using deep neural networks, they learn entity representations based on similarity between link contexts and article text in Wikipedia.  ... 
doi:10.1162/tacl_a_00129 fatcat:gsykvrjypzg4fpsnwdq5uew4ma

Natural Language Interfaces to Data

Abdul Quamar, Vasilis Efthymiou, Chuan Lei, Fatma Özcan
2022 Foundations and Trends in Databases  
There are three main challenges in natural language querying: (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying  ...  There are two main approaches in the literature for interpreting a user's natural language query. Rule-based systems make use of semantic indices, ontologies, and knowledge  ...  With the advances in deep learning-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using deep learning models.  ... 
doi:10.1561/1900000078 fatcat:fcm4gxlghbdejjpdk64pt3l6oa

Knowledge harvesting in the big-data era

Fabian Suchanek, Gerhard Weikum
2013 Proceedings of the 2013 international conference on Management of data - SIGMOD '13  
Idea: Use deep linguistic parsing to define patterns Deep linguistic patterns work even on sentences with variations Paris, the French capital, lies on the beautiful banks of the Seine Entity Name Disambiguation  ...  Harness initial KB for distant supervision & efficiency: seeds from KB, canonicalized entities with type contraints Hand-crafted domain models are assets: expressive constraints are vital, modeling  ...  The higher the inverse functionality of r for r(x,y), r(x',y), the higher the likelihood that x=x'. = ⇒ = ′ PARIS matches YAGO and DBpedia  ... 
doi:10.1145/2463676.2463724 dblp:conf/sigmod/SuchanekW13 fatcat:bl5wgfwvnbftfeug6havzwobfa

Resolving polysemy and pseudonymity in entity linking with comprehensive name and context modeling

Zhao-Yan Ming, Tat Seng Chua
2015 Information Sciences  
Specially, we harness entity coreferences within query and KB documents together with the external alias resources for modeling name variants, and further use the name variants to identify focused context  ...  heterogeneous aspects of the query and KB context.  ...  For context modeling, we further utilized the coreferent results and extracted heterogeneous aspects of a query and an entity's context for enhanced disambiguation.  ... 
doi:10.1016/j.ins.2015.02.025 fatcat:cs2auka3afggflhcxawgjsxcla

An Imperative Focus on Semantic Web Principles, Logics and its Application

Senthil Kumar N, Dinakaran M
2015 The International Journal of Ambient Systems and Applications  
data models such as RDF/RDFS.  ...  So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for  ...  and found mismatching the web query.  In the context of any text, subsequent presence of some names might be abbreviated.  ... 
doi:10.5121/ijasa.2014.3101 fatcat:7mr7rsq23rfrnlluzhy4xm4a3e

Robust and Collective Entity Disambiguation through Semantic Embeddings

Stefan Zwicklbauer, Christin Seifert, Michael Granitzer
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disambiguation.  ...  Our approach is also able to abstain if no appropriate entity can be found for a specific surface form.  ...  Semantic embeddings have also been used for entity disambiguation. In 2013, He et al. [13] proposed an entity disambiguation model, based on Deep Neural Networks.  ... 
doi:10.1145/2911451.2911535 dblp:conf/sigir/ZwicklbauerSG16 fatcat:dzv7i6i4yzg67l3z6mptnf5abi

A Recurrent Model for Collective Entity Linking with Adaptive Features

Xiaoling Zhou, Yukai Miao, Wei Wang, Jianbin Qin
Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models.  ...  We propose novel adaptive features that focus on extracting discriminative features to better model similarities between candidate entities and the mention's context.  ...  The Titan V used for this research was donated by the NVIDIA Corporation.  ... 
doi:10.1609/aaai.v34i01.5367 fatcat:ljpdrdnf55gyharv7i6t6yhggm

A Sequence Learning Method for Domain-Specific Entity Linking

Emrah Inan, Oguz Dikenelli
2018 Proceedings of the Seventh Named Entities Workshop  
Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation.  ...  Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model.  ...  Most recent deep learning approaches have been presented as a way to support better generalization for the similarity measurement of context, mention and entity (Sun et al., 2015) .  ... 
doi:10.18653/v1/w18-2403 dblp:conf/aclnews/InanD18 fatcat:jec5nd4nnrcmhbzn33oob7lk64

Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service

Luca Foppiano, Laurent Romary
2020 Journal of the Japanese Association for Digital Humanities  
The representation is also compliant with the Web Annotation Data Model (WADM).  ...  queries.  ...  Web Annotation Data Model 21 .  ... 
doi:10.17928/jjadh.5.1_22 fatcat:noxggjlhljbqbm3ibwm2p3tjiu

Applying Semantic Role Labeling and Spreading Activation Techniques for Semantic Information Retrieval

Tomas Vileiniskis, Rita Butkiene
2020 Information Technology and Control  
Finally, we present an assessment on the applicability of our method for semantically enhanced IR.  ...  First, we apply semantic role labeling (SRL) to automatically extract event-oriented information found in natural language texts to an RDF knowledge graph leveraging semantic web technology.  ...  We will refer to these Figure 1 Conceptual model for SRL-based information retrieval pipeline of three main NLP components: semantic role labeler, named entity tagger and disambiguator (entity KB linker  ... 
doi:10.5755/j01.itc.49.2.24985 fatcat:yufgrnnbbvcmxbvqug575fnllu

Scalable Disambiguation System Capturing Individualities of Mentions [chapter]

Tiep Mai, Bichen Shi, Patrick K. Nicholson, Deepak Ajwani, Alessandra Sala
2017 Lecture Notes in Computer Science  
In this paper, we propose a new system that learns specialized features and models for disambiguating each ambiguous phrase in the English language.  ...  Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications.  ...  However, due to the utilization of pairwise wordentity, entity-entity interactions or even combinatorial interactions, many joint disambiguation approaches suffer from slow query time.  ... 
doi:10.1007/978-3-319-59888-8_31 fatcat:usnyp3ckhzhkbc2reqoytwtgou

Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation [article]

Hongzhao Huang and Larry Heck and Heng Ji
2015 arXiv   pre-print
This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling  ...  Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic.  ...  Then we apply the approach to model topical coherence for entity disambiguation, as opposed to Web search.  ... 
arXiv:1504.07678v1 fatcat:ykfpeyk3ujg7vghjeg6krabe2i

Neural entity linking: A survey of models based on deep learning

Özge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann, Mehwish Alam, Davide Buscaldi, Michael Cochez, Francesco Osborne, Diego Reforgiato Recupero, Harald Sack
2022 Semantic Web Journal  
The vast variety of modifications of this general architecture are grouped by several common themes: joint entity mention detection and disambiguation, models for global linking, domain-independent techniques  ...  Since many neural models take advantage of entity and mention/context embeddings to represent their meaning, this work also overviews prominent entity embedding techniques.  ...  The work of Artem Shelmanov in the current study (preparation of sections related to application of entity linking to neural language models, entity ranking, context-mention encoding, and overall harmonization  ... 
doi:10.3233/sw-222986 fatcat:6gwmbtev7ngbliovf6cpf5hyde

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Xiaofeng Liao, Zhiming Zhao
2019 ACM Computing Surveys  
Link to publication Creative Commons License (see CC BY Citation for published version (APA):  ...  ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their helpful comments, in addition to Cees de Laat, Paul Martin, Jayachander Surbiryala, and ZeShun Shi for useful discussions.  ...  model for entity linking.  ... 
doi:10.1145/3324473 fatcat:fg5ucwtloze6ljdlh4hqjkqxfe
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