Glossary

Anaphora Resolution: Anaphora resolution refers to linking pronouns such as “his”, “her”, “their”, and “it” to the correct people, places, or things mentioned earlier in a piece of text.

Data Lineage: The analytical “path” that led to a certain conclusion. This information can be critical to the proper understanding of information being analyzed. Also refers to the path that a certain data element or value took all the way from source(s), through various transformations, to the resulting analysis.

Data Mining Tools: Tools used for pattern detection, anomaly detection, and data prediction against large sets of numerical or structured data.

Data Visualization and Mapping Tools: Tools used for visually describing, presenting, and analyzing data. For example, Clarabridge Navigator™, visually shows the linguistic connections in a dataset, structured attributes, and categories.

Data Warehouse (DW): A data warehouse is a database that contains a record of an organizations’ past transactional and operational information designed for efficient data analysis and reporting.

Entity extraction: Determining all people, places, objects, etc. within a document. Tools include Inxight, Aerotext, Lingpipe, GATE, and NetOwl.

Entity & Fact extraction: Identifies named entities with associated facts and events. Uncovers critical links between structured and unstructured data for deeper understanding.

Experience Data Warehouse: A data warehouse that contains the experiential data captured from consumer or employee feedback.

Extract, Transform, Load (ETL): The process whereby structured data is sourced from multiple data repositories; transformed to allow it to be cleansed, merged with other data, or manipulated for analytical purposes; and loaded into a data warehouse for analysis.

Machine Learning: Machine Learning is an algorithm that extends the recall of your Classification Model.  Machine Learning uses classified sentences as a training set, and assigns previously unclassified sentences based on a confidence threshold about how similar they are to the sentences in your training set.  

Online Analytical Processing (OLAP): Sometimes called dimensional analysis, OLAP, is the process of slicing data by various dimensions (time, dollars, product line, etc.) to see summary and detailed data for decision making.

Ontology: An understanding of the “fundamental categories of things in the world.” For analysis purposes, ontologies can help to group various entities to better understand the context of that entity within a broader domain and how it relates to other entities. They can also be used to filter out certain types of entities that are undesired for a particular analysis.

Semantic Distillation: Essentially, this means that all available information is sourced from the content as efficiently as possible using a number of pre-configured transformations. Although a certain threshold of quality is required, quantity is typically desired over exacting quality for applications using semantic distillation.

Sentiment extraction: Isolates and identifies attitudes, perceptions and feelings as expressed by consumer feedback, a clear understanding of how customers think and feel is revealed. Sentiment defines the feeling associated with words in a sentence.

Structured Data: Content which has structure that is easily interpreted by a machine, commonly in a database or XML format.

Structured Data Integrator: Clarabridge provides a tool which combines structured, or quantitative, data with the unstructured text-based information to enable users to receive a complete 360° view of their customers’ experiences.

Taxonomy: A hierarchy of “things.” For example, a geographical taxonomy may include relationships between a city, state, and country. This is useful for “drilling into” data from a higher level down to lower levels of detail.

Text analysis or Text Mining: An application used for extracting meaning from content. For example, part-of speech detection, grammatical parsing and named-entity recognition.