Review of Text analytics 2011 talk by Seth Grimes
Text analysis adds value where transactional information stops. From the information retrieval perspective, people want to publish, manage, archive, index and search, categorize and classify and extract metadata. Text analytics add semantic understanding of named entities, pattern based entities, concepts, facts and relationships, concrete and abstract attributes, subjectivity etc. Text analytics applications lets users search terms, retrieve material from large scale structures, search features such as entities or topics, retrieve materials such as facts and relationships, group results based on topics and visually explore information. Some examples are SiloBreaker, FirstRain, Bing, Google. etc. Text analytics includes metadata and metadata population Search results are measued based on precision and recall. Accuracy is measured with the combination of the two in a term called f-score which is defined as 2 * (precision * recall)/ (precision + recall). Typical steps in text analytics include : identify and retrieve documents for analysis, apply techniques to discern, tag and extract entities and apply techniques to classify documents and organize extracted features. BeyeNetwork and Ranks.NL are some examples of these. Applications such as Connexor  and VisuWords talk display part of speech tagging and ontology. Search logs suggest that