Sökning: "entity extraction"
Visar resultat 1 - 5 av 33 uppsatser innehållade orden entity extraction.
1. Data Augmentation: Enhancing Named Entity Recognition Performance on Swedish Medical Texts
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : Named Entity Recognition (NER) refers to the task of locating relevant information within text sequences. Within the medical domain, it can benefit applications such as de-identifying patient records or extracting valuable data for other downstream tasks. LÄS MER
2. Fine-tuning a BERT-based NER Model for Positive Energy Districts
Master-uppsats, Högskolan Dalarna/Institutionen för information och teknikSammanfattning : This research presents an innovative approach to extracting information from Positive Energy Districts (PEDs), urban areas generating surplus energy. PEDs are integral to the European Commission's SET Plan, tackling housing challenges arising from population growth. LÄS MER
3. Named Entity Recognition for Case Narratives of Adverse Event Reports
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : In the field of pharmacovigilance (PV), signal detection and assessment activities play a crucial role. They require a PV assessor to read through countless adverse event reports which is manual labor-intensive work. To ease the reading process, visual highlighters can be provided by leveraging natural language processing techniques. LÄS MER
4. Exploring Data Extraction and Relation Identification Using Machine Learning : Utilizing Machine-Learning Techniques to Extract Relevant Information from Climate Reports
Kandidat-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Ensuring the accessibility of data from Swedish municipal climate reports is necessary for examining climate work in Sweden. Manual data extraction is time-consuming and prone to errors, necessitating automation of the process. LÄS MER
5. CONNECTING THE DOTS : Exploring gene contexts through knowledge-graph representations of gene-information derived from scientific literature
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Analyzing the data produced by next-generation sequencing technologies relies on access to information synthesized based on previous research findings. The volume of data available in the literature is growing rapidly, and it is becoming increasingly necessary for researchers to use AI or other statistics-based approaches in the analysis of their datasets. LÄS MER