High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data
The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm - a high-throughput, lightweight, language-agnostic EDL system that extracts and correctly disambiguates 75% more entities than state-of-the-art EDL systems and is significantly faster than them.
Code (0)
등록된 구현이 없습니다.
Tasks
Entity DisambiguationInformation RetrievalRetrievalText CategorizationSimilar Papers 제목 키워드 기반
Entity Linking with a Paraphrase Flavor
The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the mai…
ClusteringDiagnosticEntity DisambiguationEntity LinkingContextual Augmentation for Entity Linking using Large Language Models
Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, whi…
Entity DisambiguationEntity LinkingAn Unsupervised Language-Independent Entity Disambiguation Method and its Evaluation on the English and Persian Languages
Entity Linking is one of the essential tasks of information extraction and natural language understanding. Entity linking mainly consists of two tasks: recognition and disambiguation of named entities. Most studies addre…
Entity DisambiguationEntity LinkingNatural Language UnderstandingEntity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
We present a new local entity disambiguation system. The key to our system is a novel approach for learning entity representations. In our approach we learn an entity aware extension of Embedding for Language Model (ELMo…
Entity DisambiguationLanguage ModelingLanguage ModellingUnderstanding Why Language Models Hallucinate: Testing Reasoning Against Priors
Large language models often produce hallucinated answers that violate prompt-level constraints. A key diagnostic question is whether these failures reflect missing knowledge, or whether the model has the relevant informa…
Entity Disambiguation