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.
Clarabridge Navigator™: Enables non-technical users to adjust and tune business views “on-the-fly”, whenever business, market or other customer experience conditions change. The Navigator is a web-based user interface where Classification and Sentiment are maintained.
Clustering: Automatically sorts interesting themes within a particular category, which helps create sub categories and highlights commonalities within a particular category. This feature enables an understanding of unknown causes or “other” categories.
Concept extraction: Also known as topic extraction, concept extraction involves understanding the underlying concept that a document or section of a document is describing. Techniques, such as automatically applying categorizations can be used for concept extraction.
Clarabridge Content Mining Platform™ (CMP): A text mining platform that provides a direct conduit between your company and the current, authentic opinions and attitudes of your customers, which are revealed in a range of listening posts from across your enterprise. Deployed behind a corporate firewall, integrated source connectors blend key customer insights from both unstructured and structured data sources to create a more integrated perspective on customer experience.
Content Mining Service (CMS): Subscription-based software-as-a-service (SaaS) text mining platform that gives a company instant access to their dashboards and reports over a secure internet connection with the same powerful functionality found in the Clarabridge Content Mining Platform™. All data management and report generation is managed by Clarabridge, with our consultants serving as an extension of your team to create and maintain the CMS reporting interface.
Customer Experience Management (CEM): The practice of actively listening to the Voice of the Customer through a variety of listening posts, analyzing the customer feedback to create a basis for acting on better business decisions and then measuring the impact of those decisions to drive even greater operational performance and customer loyalty.
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, as part of CMP, 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.