named-entity-recognition-ner-challenges-and-model Blog Related Topics 
Ontotext
JANUARY 10, 2025
Introduction In the real world, obtaining high-quality annotated data remains a challenge. Generative AI (GenAI) models, such as GPT-4, offer a promising solution, potentially reducing the dependency on labor-intensive annotation. This blog post summarizes our findings, focusing on NER as a first-step key task for knowledge extraction.
Sisense
MAY 26, 2020
Programmers encounter many common challenges when trying to teach computers to understand natural language text data. In this post, we’ll discuss these challenges in detail and include some tips and tricks to help you handle text data more easily. Most common challenges we face in NLP are around unstructured data and Big Data.
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Ontotext
MARCH 15, 2024
Challenges Medical multilingual question answering (QA) presents several challenges stemming from the diverse nature of medical terminologies and linguistic variations. Furthermore, as the clinical data is highly sensitive, there are no open-access models or datasets available to solve the task, especially in the multilingual setting.
Ontotext
MARCH 7, 2024
To help in the battle against disinformation, Ontotext is tackling the challenge of identifying narratives or disinformation campaigns. According to recent publications for entity linking , Wikipedia and Wikidata are among the most popular ones. Wikidata is the biggest public knowledge graph, covering over 100 million entities.
Domino Data Lab
JULY 1, 2021
In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. What is Named Entity Recognition?
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