Entity linking
View article: Representation Learning of Knowledge Graphs with Entity Descriptions
Representation Learning of Knowledge Graphs with Entity Descriptions Open
Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between …
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Local and global algorithms for disambiguation to Wikipedia Open
Disambiguating concepts and entities in a context sensitive way is a fundamental problem in natural language processing. The comprehensiveness of Wikipedia has made the online encyclopedia an increasingly popular target for disambiguation.…
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Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks Open
Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the…
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A Unified MRC Framework for Named Entity Recognition Open
The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not.Models are usually separately developed for the two tasks, since sequence labeling models, th…
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Deep learning with word embeddings improves biomedical named entity recognition Open
Motivation Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pr…
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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention Open
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats…
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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment Open
Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alig…
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Fine-Grained Entity Recognition Open
Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g.,…
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Named Entity Recognition as Dependency Parsing Open
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the f…
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Iterative Entity Alignment via Joint Knowledge Embeddings Open
Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature constru…
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Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition Open
This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recal…
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Adaptive Co-attention Network for Named Entity Recognition in Tweets Open
In this study, we investigate the problem of named entity recognition for tweets. Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades. Previous named entity recognit…
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Entity-Relation Extraction as Multi-Turn Question Answering Open
In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and elations is transformed to the task of identifying answe…
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Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation Open
Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia).In this paper, we propose a novel embedding method specif…
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Entity Alignment between Knowledge Graphs Using Attribute Embeddings Open
The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a k…
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Multi-view Knowledge Graph Embedding for Entity Alignment Open
We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refine…
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Entity Linking via Joint Encoding of Types, Descriptions, and Context Open
For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts fro…
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End-to-End Neural Entity Linking Open
Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual…
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Deep Exhaustive Model for Nested Named Entity Recognition Open
We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of …
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Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment Open
Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely lea…
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Ultra-Fine Entity Typing Open
We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formul…
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Improving Entity Linking by Modeling Latent Relations between Mentions Open
Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base. Entity linking systems often exploit relations between textual mentions in a document (e.g., coreference) to decide if …
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Autoregressive Entity Retrieval Open
Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article). The ability to retrieve such entities given a query is fund…
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Named Entity Extraction for Knowledge Graphs: A Literature Overview Open
An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. N…
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UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification Open
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale da…
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Cheap Translation for Cross-Lingual Named Entity Recognition Open
Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cros…
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CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning Open
Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects…
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Neural entity linking: A survey of models based on deep learning Open
This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the “deep learning revolution” in natural language processing. Its goal is to systemize design features of ne…
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Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media Open
In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on N…
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Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader Open
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.Under the assumptions that structured data is easier to query and t…