dokumentsegmentering

Dokumentsegmentering is the process of dividing a document into meaningful units such as sections, topics, paragraphs, or discourse segments. The goal is to identify boundaries between logical units to support downstream tasks such as retrieval, summarization, editing, and analysis. The term is used in information retrieval, natural language processing, and digital humanities.

There are several approaches to dokumentsegmentering. Rule-based methods use typography, headings, numbering, and punctuation cues or explicit formatting to locate segment boundaries. Statistical and machine-learning methods learn boundary positions from labeled data, using features such as lexical similarity, sentence length, or cue phrases. Unsupervised approaches rely on discourse or topic models to detect shifts in content. In discourse-oriented segmentation, frameworks like Rhetorical Structure Theory may be employed. Modern methods frequently apply neural models that predict boundary positions from sentence representations or perform sequence labeling with encoders such as BERT.

Evaluation typically uses boundary-based metrics such as precision, recall, and F1 with a tolerance window for small boundary shifts. Content-based evaluations may consider how well segments preserve meaning or support downstream tasks. Applications include improving search indexing, facilitating reading on long documents, segment-level summarization, and structured editing or redaction. Segment definitions can vary by language and domain, and noisy text from OCR or scanned PDFs presents additional challenges.

Future directions include cross-document and cross-lingual segmentation, segmenting at finer granularity such as rhetorical units, and tighter integration with downstream NLP pipelines. Availability of benchmarks and open corpora supports progress, while robustness to noisy input and multi-modal data remains an active area.