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Found 16 papers, 6 papers with code
Unsupervised Multi-View Post-OCR Error Correction With Language Models
We investigate post-OCR correction in a setting where we have access to different OCR views of the same document.
Large Process Models: Business Process Management in the Age of Generative AI
The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness.
Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?
$\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology.
HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG).
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Our empirical study was conducted on two well-known knowledge bases (i. e., Wikidata and Wikipedia).
Ranked #1 on
Entity Linking
on MSNBC
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input.
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora
Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval.
A Study of the Importance of External Knowledge in the Named Entity Recognition Task
In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER).
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Robust Disambiguation of Named Entities in Text
Disambiguating named entities in naturallanguage text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO.
Ranked #15 on
Entity Linking
on AIDA-CoNLL
