Named Entity Linking on Namesakes

• URL: http://arxiv.org/abs/2205.10498v1
• Date: Sat, 21 May 2022 03:31:25 GMT
• Title: Named Entity Linking on Namesakes
• Authors: Oleg Vasilyev, Alex Dauenhauer, Vedant Dharnidharka, John Bohannon
• Abstract summary: We represent knowledge base (KB) entity by a set of embeddings. We show that representations of entities in the knowledge base (KB) can be adjusted using only KB data, and the adjustment improves NEL performance.
• Score: 10.609815608017065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a simple and practical method of named entity linking (NEL), and explore its features and performance on a dataset of ambiguous named entities - Namesakes. We represent knowledge base (KB) entity by a set of embeddings. Our observations suggest that it is reasonable to keep a limited number of such embeddings, and that the number of mentions required to create a KB entity is important. We show that representations of entities in the knowledge base (KB) can be adjusted using only KB data, and the adjustment improves NEL performance.

Related papers

• OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting [49.655711022673046]
  OneNet is an innovative framework that utilizes the few-shot learning capabilities of Large Language Models (LLMs) without the need for fine-tuning. OneNet is structured around three key components prompted by LLMs: (1) an entity reduction processor that simplifies inputs by summarizing and filtering out irrelevant entities, (2) a dual-perspective entity linker that combines contextual cues and prior knowledge for precise entity linking, and (3) an entity consensus judger that employs a unique consistency algorithm to alleviate the hallucination in the entity linking reasoning.
  arXiv  Detail & Related papers  (2024-10-10T02:45:23Z)
• Exploring Partial Knowledge Base Inference in Biomedical Entity Linking [0.4798394926736971]
  We name this scenario partial knowledge base inference. We construct benchmarks and witness a catastrophic degradation in EL performance due to dramatically precision drop. We propose two simple-and-effective redemption methods to combat the NIL issue with little computational overhead.
  arXiv  Detail & Related papers  (2023-03-18T04:31:07Z)
• NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker [2.3605348648054463]
  We introduce an EL approach that is aware of NIL-entities and produces corresponding mention clusters while maintaining high linking performance for known entities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL-entities.
  arXiv  Detail & Related papers  (2023-03-08T08:08:57Z)
• Disambiguation of Company names via Deep Recurrent Networks [101.90357454833845]
  We propose a Siamese LSTM Network approach to extract -- via supervised learning -- an embedding of company name strings. We analyse how an Active Learning approach to prioritise the samples to be labelled leads to a more efficient overall learning pipeline.
  arXiv  Detail & Related papers  (2023-03-07T15:07:57Z)
• Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking [23.01938139604297]
  We propose a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a NIL entity. Results on five datasets show the advantages of BLINKout over existing methods to identify out-of-KB mentions.
  arXiv  Detail & Related papers  (2023-02-14T17:00:06Z)
• Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database [86.03294330305097]
  We propose a unified semantic element for question answering (QA) on both knowledge bases (KB) and databases (DB) We introduce the primitive (relation and entity in KB, table name, column name and cell value in DB) as an essential element in our framework. We leverage the generator to predict final logical forms by altering and composing topranked primitives with different operations.
  arXiv  Detail & Related papers  (2022-11-09T19:33:27Z)
• Visual Named Entity Linking: A New Dataset and A Baseline [61.38231023490981]
  We consider a purely Visual-based Named Entity Linking (VNEL) task, where the input only consists of an image. We propose three different sub-tasks, i.e., visual to visual entity linking (V2VEL), visual to textual entity linking (V2TEL), and visual to visual-textual entity linking (V2VTEL) We present a high-quality human-annotated visual person linking dataset, named WIKIPerson.
  arXiv  Detail & Related papers  (2022-11-09T13:27:50Z)
• Visual Recognition by Request [111.94887516317735]
  We present a novel protocol of annotation and evaluation for visual recognition. It does not require the labeler/algorithm to annotate/recognize all targets (objects, parts, etc.) at once, but instead raises a number of recognition instructions and the algorithm recognizes targets by request. We evaluate the recognition system on two mixed-annotated datasets, CPP and ADE20K, and demonstrate its promising ability of learning from partially labeled data.
  arXiv  Detail & Related papers  (2022-07-28T16:55:11Z)
• Improving Entity Disambiguation by Reasoning over a Knowledge Base [2.223733768286313]
  We introduce an ED model which links entities by reasoning over a symbolic knowledge base. Our model surpasses state-of-the-art baselines on six well-established ED datasets by 1.3 F1 on average.
  arXiv  Detail & Related papers  (2022-07-08T19:13:53Z)
• Number Entity Recognition [65.80137628972312]
  Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks. Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings.
  arXiv  Detail & Related papers  (2022-05-07T05:22:43Z)
• Novel Entity Discovery from Web Tables [21.16349961050804]
  We leverage tables on the Web to discover new entities, properties, and relationships. Our method identifies not only out-of-KB (novel'') information but also novel aliases for in-KB (known'') entities.
  arXiv  Detail & Related papers  (2020-02-01T13:24:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.