Kaintech accelerates dataset creation with few-shot learning Kairntech recently took part in the EvalLLM 2024 challenge on few-shot learning. The contest was organised by the French Ministry of Defense via the Direction Générale de l'Armement (DGA). The aim of this challenge was to automatically identify Named Entities in French news and blog articles. Naturally the … Continue reading EvalLLM challenge on few-shot learning: LLMs versus machine learning
Tag: Named Entity Recognition
Named Entity Recognition (NER) is a Natural Language Processing subtask. It seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories. Typical examples are Location, People and Geography.
Manual named entity recognition consists of creating a dataset and labeling entities. Through carefully labeling of a subset (dataset) of a document corpus, a model is trained.
How to annotate entities manually? – Kairntech Documentation
How to use entity suggestions? – Kairntech Documentation
How to experiment entity detection with models? – Kairntech Documentation
Models can be enriched with other means of labelling such as business vocabularies or knowledge graphs (Wikidata). Different means of labeling are combined and consolidated into AI pipelines
We are proud to announce that Kairntech wins a European Defense Fund (EDF) project. This call for projects received in total €1,031 million of EU funding. Kairntech is a leading technology provider of the NEMO consortium which is led by Sintef AS (Norway). Over the course of this 5-year project NEMO will develop a state-of-the-art … Continue reading Kairntech wins European Defense Fund project
Grenoble/Stuttgart March 6, 2023 – For their upcoming research project on Open Innovation, funded by the German Bundesland Baden-Württemberg, Fraunhofer IAO and TecIntelli have selected the AI/NLP platform from Kairntech. Kairntech will support the relevant large-scale analysis of web content and scientific publications in order to derive insights into trends and options around innovation processes. … Continue reading Kairntech chosen as NLP/AI platform to support research on Open Innovation
The Importance of Traditional NLP Use Cases: Document categorization and entity extraction NLP is booming. The pace of progress is impressive, the recognition of the field and what it has to offer is solid throughout the industry. Significant progress on issues that a few years ago where notoriously difficult to tackle: Question answering, natural language … Continue reading Document categorization and Entity Extraction: These NLP veterans are as relevant as ever
Why natural language processing (NLP) platforms save over 80% of your time Impressive recent progress in Natural Language Processing (NLP) models, for a large majority coming from the open sourced community, do not necessarily go hand in hand with delivering business value for domain experts. ROI of NLP platforms are judged by the requirement to … Continue reading ROI of NLP platforms
How to extract numbers from documents Named Entity Recognition relates to the extraction of sequences of words from within a document. The technique is mostly used to extract names of people, organisations or places, which are therefore the most typical named entities. However, the term named entity recognition does not capture very well the importance … Continue reading Extract numbers from text
Natural language processing and named entity recognition with wikidata knowledge The broad success of quantitative methods such as deep learning in NLP sometimes risks to downplay the importance of explicit, symbolic knowledge required for many NLP tasks. Good named entity recognition (NER) for instance not only needs to recognize the entities (where learning based methods … Continue reading Named Entity Recognition with Wikidata: always up to date!