Sökning: "named entity recognition"
Visar resultat 1 - 5 av 9 avhandlingar innehållade orden named entity recognition.
1. Bootstrapping Named Entity Annotation by Means of Active Machine Learning
Sammanfattning : This thesis describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. The reason for working with documents, as opposed to for instance sentences or phrases, is that the BootMark method is concerned with the creation of corpora. LÄS MER
2. Bootstrapping Named Entity Annotation by Means of Active Machine Learning: A Method for Creating Corpora
Sammanfattning : This thesis describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. The reason for working with documents, as opposed to for instance sentences or phrases, is that the BootMark method is concerned with the creation of corpora. LÄS MER
3. Extracting Clinical Findings from Swedish Health Record Text
Sammanfattning : Information contained in the free text of health records is useful for the immediate care of patients as well as for medical knowledge creation. Advances in clinical language processing have made it possible to automatically extract this information, but most research has, until recently, been conducted on clinical text written in English. LÄS MER
4. From Disorder to Order : Extracting clinical findings from unstructured text
Sammanfattning : Medical disorders and findings are examples of important information in health record text. Through developing methods for automatically extracting these entities from the health record text, the possibility of making use of the information by automatic computerised processes increases. LÄS MER
5. Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision
Sammanfattning : Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. LÄS MER