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Showing 1 to 18 of 18 for “"entity linking"”.
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Robust Entity Linking in Heterogeneous Domains
Entity Linking is the task of mapping terms in arbitrary documents to entities in a knowledge base by identifying the correct semantic meaning. It is applied in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating …
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A study of coherence in entity linking
Entity linking (EL) is the task of mapping entities, such as persons, locations, organizations, etc., in text to a corresponding record in a knowledge base (KB) like Wikipedia or Freebase. In this paper we present, for the first time, a controlled study of one aspect of this problem called …
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Advanced Methods for Entity Linking in the Life Sciences
… domain. A further part of data integration is entity resolution to build unified knowledge bases from different data sources. A data source consists of a set of records characterized by attributes. The goal of entity resolution is to identify records representing the same real-world entity. …
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Joint Biomedical Event Extraction and Entity Linking via Iterative Collaborative Training
Biomedical entity linking and event extraction are two crucial tasks to support text understanding and retrieval in the biomedical domain. These two tasks intrinsically benefit each other: entity linking disambiguates the biomedical concepts by referring to external knowledge bases and the domain …
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Enriching the Web of Data with topics and links
… from Wikipedia portals. Second, we contribute to entity linking research by presenting an optimization model for joint entity linking, showing its hardness, and proposing three heuristics implemented in the LINked Data Alignment (LINDA) system. Our first solution can exploit multi-core machines, …
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Entity Information Extraction using Structured and Semi-structured resources
… the tasks that exist in Information Extraction, Entity Linking, also referred to as entity disambiguation or entity resolution, is a new and important problem which has recently caught the attention of a lot of researchers in the Natural Language Processing (NLP) community. The task involves …
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Information extraction with weak supervision
… information extraction (IE) tasks: Named Entity Recognition (NER), Relation Extraction (RE), and Entity Linking (EL). Traditional supervised learning methods in these domains often require extensive human annotations, which are costly and time-consuming, limiting their scalability and …
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Extending Wikification: Nominal discovery, nominal linking, and the grounding of nouns
Mention discovery, entity linking, and grounding are crucial steps in natural language understanding. Compared with named entities, the detection and linking of nominals are relatively little studied but essential since the grounding of nouns enriches information for humans that read documents. In …
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Towards Knowledge Graph Construction From Unstructured Text with LLMs, Triple Identification and Alignment to Wikidata
… KGs, particularly in the accurate extraction and linking of knowledge triples. This thesis addresses these challenges by presenting a comprehensive framework for extracting high-quality subject-predicate-object triples from unstructured text and linking them to a structured KG. To tackle the …
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Towards Knowledge Graph Construction From Unstructured Text with LLMs, Triple Identification and Alignment to Wikidata
… KGs, particularly in the accurate extraction and linking of knowledge triples. This thesis addresses these challenges by presenting a comprehensive framework for extracting high-quality subject-predicate-object triples from unstructured text and linking them to a structured KG. To tackle the …
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Evaluating automated and hybrid neural disambiguation for African historical named entities
… This problem may be alleviated by using a Named Entity Disambiguation (NED) system to disambiguate names by linking them to a knowledge base. In recent years, transformer-based language models have led to improvements in NED systems. Furthermore, multilingual language models have shown the …
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Exploiting Cross-Lingual Representations For Natural Language Processing
… through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest.
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Cross-lingual entity extraction and linking for 300 languages
The student, Xiaoman Pan, accepted the attached license on 2020-12-02 at 17:38.
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Knowledge base integration in biomedical natural language processing applications
… later demonstrate the importance of a (lacking) entity linking system to perform optimal integration of biomedical knowledge bases, and we offer a first stride towards solving that problem, along with conclusions on proper training setup and processes for automatic collection of an annotated …
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Towards Generalizable Information Extraction with Limited Supervision
… we define a new multimodal IE task that links an entity mention within heterogeneous information sources to a knowledge base with limited annotation data. We demonstrate that excellent multimodal IE performance can be achieved, even with limited annotation data, by leveraging monomodal external …
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Leveraging Knowledge Bases in Web Text Processing
The Web contains more text than any other source in human history, and continues to expand rapidly. Computer algorithms to process and extract knowledge from Web text have the potential not only to improve Web search, but also to collect a sizable fraction of human knowledge and use it to enable …
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Concept and entity grounding using indirect supervision
… English and the goal is to identify concept and entity mentions, and find the corresponding entries the mentions refer to in Wikipedia. We extend this problem in two directions: First, we study identifying and grounding entities written in any language to the English Wikipedia. Second, we …
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Neural Sequence Modeling for Domain-Specific Language Processing: A Systematic Approach
In recent years, deep learning based sequence modeling (neural sequence modeling) techniques have made substantial progress in many tasks, including information retrieval, question answering, information extraction, machine translation, etc. Benefiting from the highly scalable attention-based …