Information Extraction – Named Entity Recognition Sample Clauses

Information Extraction – Named Entity Recognition. ‌ A particular scenario of information extraction is the recognition of entity references (such as authors, headings, citations, persons, organizations, places, time periods, etc.) and their resolution to real world entities. In this scenario, the entity references are found within unstructured text or in semi-structured data fields. The typical output of this scenario is the exact location of where the entities are mentioned within the data, and their resolution to a set of entities known to be disambiguated – text is enriched with appropriate tags or attributes. Previous research on information extraction has focused mainly on natural lan- guage processing. Information extraction processes are composed of subtasks, each task simplifies the text by transforming it into more machine processable structures. So, for example, before entities can be recognized it may be necessary to identify the language of the text, to tokenize the text into paragraphs, sentences and words, and to classify the words for their part-of-speech category. It is by reasoning on the output of this process that references to entities are identified. This process is therefore dependent on evidence given by the natural language text to identify a reference to an entity, and also its type. Information extraction relies mainly on natural language processing. It consists of a process composed of subtasks that simplify the text by transforming it into more machine processable structures. Some of the typical information extraction subtasks are: Entity recognition: recognition of entity names (persons, organizations, places, temporal expressions, numerical expressions, etc.). works for organization ). Entity recognition is one of the major subtasks of information extraction. Because of the complexity of the problem, entity recognition is typically studied on restricted domains. The design of an entity resolution system needs to take into considerations the characteristics of the corpus where it is to be applied, such as the languages, textual genres, domains and types of entities. In general terms, any entity recognition approach consists of a two step process. It starts with the identification of pieces of information which are relevant for solving the entity recognition problem, and then follows by reasoning on the available information to make the decision of recognizing an entity and its type. These pieces of information are typically referred to as features, clues [51] or evidences [18]...
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  • Billing Information 6.1 NLT and the RL shall provide each other with information within their possession that is necessary to allow them to provide accurate and timely billing to each other and to any other relevant third parties.

  • Enterprise Information Management Standards Grantee shall conform to HHS standards for data management as described by the policies of the HHS Office of Data, Analytics, and Performance. These include, but are not limited to, standards for documentation and communication of data models, metadata, and other data definition methods that are required by HHS for ongoing data governance, strategic portfolio analysis, interoperability planning, and valuation of HHS System data assets.

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