Entity Disambiguation

Entity disambiguation resolves the many- to-many correspondence between mentions of entities in text and unique real-world en- tities. Fair Isaac’s entity disambiguation uses language-independent entity context to agglomeratively resolve mentions …...

This paper describes a language-independent, scalable system for both challenges of cross- document co-reference: name variation and entity disambiguation. We provide system re- sults from the ACE 2008 evaluation in …...

Ambiguity of entity mentions and con- cept references is a challenge to mining text beyond surface-level keywords. We describe an effective method of disambi- guating surface forms and resolving them …...

Information extraction from microblog posts is an important task, as today microblogs cap- ture an unprecedented amount of information and provide a view into the pulse of the world. As …...

AbstractEntity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, …...

AbstractThe 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 …...

AbstractRich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. …...

AbstractIn this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we …...

AbstractWe propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We …...

AbstractRetrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark …...

AbstractGenerative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL …...

AbstractLocal models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models. However, despite their significant performance achievements, most …...

AbstractRecent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types. …...

AbstractIn this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to …...

AbstractQuestion answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have …...

AbstractEntity Linking performance has a strong reliance on having a large quantity of high-quality annotated training data available. Yet, manual annotation of named entities, especially their boundaries, is ambiguous, error-prone, …...

AbstractThere has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks. The conventional mechanism to retrieve facts in KGs usually involves three steps: …...

AbstractLocal models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions. However, previous works have so far limited their …...

AbstractLarge pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection into Language Models (KILM), a novel …...

AbstractEntity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base. Due to its industrial relevance, as well as current progress …...