Tekstitietoa

Tekstitietoa refers to information embedded in written or printed language. It includes facts, events, entities, concepts, and subjective opinions described in text, as well as descriptive data conveyed through narrative or exposition. In information science and linguistics, tekstitietoa is contrasted with non-textual data such as images, audio, or structured databases. The analysis of tekstitietoa aims to identify, extract, and organize information in a form usable for search, retrieval, and analytics.

In practice, tekstitietoa is extracted through natural language processing and text mining methods. Key types include named entities (such as people, places, organizations), relationships among entities, events and dates, quantities, and sentiment or stance. Core techniques include tokenization, part-of-speech tagging, named entity recognition, syntactic parsing, coreference resolution, relation extraction, topic modeling, and supervised or unsupervised learning.

Applications span digital libraries and archives, search engines, content management and enterprise analytics, customer feedback analysis, and monitoring of social media or news. Tekstitietoa also supports compliance, risk assessment, and linguistic research in corpus linguistics and the humanities.

Challenges in working with tekstitietoa include linguistic ambiguity and polysemy, multilingual and low-resource languages, OCR or transcription errors in digitized texts, privacy and ethical considerations, and biases in data and models. Ongoing work seeks to improve accuracy, context understanding, and interoperability across languages and domains.

See also natural language processing, information extraction, text mining, and data science.