Papers Entity Retrieval
“Entity Retrieval” 태그가 달린 논문 56편 · 필터 해제
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval
While significant progress has been made with dual- and bi-encoder dense retrievers, they often struggle on queries with logical connectives, a use case that is often overlooked yet important in downstream applications. …
Contrastive LearningEntity RetrievalRetrievalER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources
Large language models (LLMs) excel in question-answering (QA) tasks, and retrieval-augmented generation (RAG) enhances their precision by incorporating external evidence from diverse sources like web pages, databases, an…
Entity RetrievalKnowledge GraphsQuestion AnsweringRAG+2Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation
Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements at test time…
Entity RetrievalFormKnowledge Base Question AnsweringQuestion Answering+1Annotative Indexing
This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the …
Entity RetrievalKnowledge GraphsRetrievalRetrieval-augmented GenerationDyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval accuracy and limits the model's ability…
Document RankingEntity EmbeddingsEntity RetrievalRetrieval+1Entity Retrieval for Answering Entity-Centric Questions
The similarity between the question and indexed documents is a crucial factor in document retrieval for retrieval-augmented question answering. Although this is typically the only method for obtaining the relevant docume…
Entity RetrievalQuestion AnsweringRetrievalDERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs
Entity Alignment (EA) aims to match equivalent entities in different Knowledge Graphs (KGs), which is essential for knowledge fusion and integration. Recently, embedding-based EA has attracted significant attention and m…
AttributeEntity AlignmentEntity EmbeddingsEntity Retrieval+2GRAG: Graph Retrieval-Augmented Generation
Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in many applications such as citation graph…
Entity RetrievalKnowledge GraphsRAGRetrieval+1REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer …
Benchmarkingcoreference-resolutionCoreference ResolutionDocument-level Closed Information Extraction+11SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrieval
Clinician must write a lengthy summary each time a patient is discharged from the hospital. This task is time-consuming due to the sheer number of unique clinical concepts covered in the admission. Identifying and coveri…
Entity RetrievalRetrievalSentenceOn Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval
Visually-rich document entity retrieval (VDER), which extracts key information (e.g. date, address) from document images like invoices and receipts, has become an important topic in industrial NLP applications. The emerg…
Contrastive LearningEntity RetrievalFew-Shot LearningMeta-Learning+1Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given input document and then link the mentions t…
Entity LinkingEntity RetrievalKnowledge GraphsMachine Reading Comprehension+2DocumentNet: Bridging the Data Gap in Document Pre-Training
Document understanding tasks, in particular, Visually-rich Document Entity Retrieval (VDER), have gained significant attention in recent years thanks to their broad applications in enterprise AI. However, publicly availa…
document understandingEntity RetrievalFew-Shot LearningRetrieval+1Task Oriented Conversational Modelling With Subjective Knowledge
Existing conversational models are handled by a database(DB) and API based systems. However, very often users' questions require information that cannot be handled by such systems. Nonetheless, answers to these questions…
Entity RetrievalKeyword Extractionnamed-entity-recognitionNamed Entity Recognition+4KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system to produce satisfactory responses. In th…
Entity RetrievalFew-Shot LearningRe-RankingRetrievalModeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sen…
Entity RetrievalGraph AttentionLanguage ModelingLanguage Modelling+3OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
Medical entity span extraction and linking are critical steps for many healthcare NLP tasks. Most existing entity extraction methods either have a fixed vocabulary of medical entities or require span annotations. In this…
Entity LinkingEntity RetrievalRetrievalNo Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
Recent work has shown that small distilled language models are strong competitors to models that are orders of magnitude larger and slower in a wide range of information retrieval tasks. This has made distilled and dense…
Argument RetrievalBiomedical Information RetrievalCitation PredictionDuplicate-Question Retrieval+7R2D2: Robust Data-to-Text with Replacement Detection
Unfaithful text generation is a common problem for text generation systems. In the case of Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial for any real-world applications. We intr…
Data-to-Text GenerationEntity RetrievalNERRetrieval+1Entity-aware Transformers for Entity Search
Pre-trained language models such as BERT have been a key ingredient to achieve state-of-the-art results on a variety of tasks in natural language processing and, more recently, also in information retrieval.Recent resear…
Entity EmbeddingsEntity RetrievalKnowledge GraphsLanguage Modelling+2