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    Named Entity Recognition: Fundamentals and Applications
    Named Entity Recognition: Fundamentals and Applications
    Named Entity Recognition: Fundamentals and Applications
    Ebook108 pages1 hour

    Named Entity Recognition: Fundamentals and Applications

    By Fouad Sabry

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    About this ebook

    What Is Named Entity Recognition


    Named-entity recognition, or NER, is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, and so on. Other names for this subtask include (named) entity identification, entity chunking, and entity extraction. Named-entity recognition is also known as named-entity identification.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Named-entity recognition


    Chapter 2: Natural language processing


    Chapter 3: Information extraction


    Chapter 4: Named entity


    Chapter 5: Relationship extraction


    Chapter 6: Outline of natural language processing


    Chapter 7: Entity linking


    Chapter 8: Apache cTAKES


    Chapter 9: SpaCy


    Chapter 10: Zero-shot learning


    (II) Answering the public top questions about named entity recognition.


    (III) Real world examples for the usage of named entity recognition in many fields.


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of named entity recognition' technologies.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of named entity recognition.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJul 5, 2023
    Named Entity Recognition: Fundamentals and Applications

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      Book preview

      Named Entity Recognition - Fouad Sabry

      Chapter 1: Named-entity recognition

      Named-entity recognition (NER), also known as (named) entity identification, entity chunking, and entity extraction, is a subtask of information extraction that aims to identify and categorize named entities (such as people, places, things, ideas, concepts, dates, times, money, etc.) mentioned in unstructured text into pre-defined categories.

      Most studies of NER/NEE systems have followed the format of an unannotated text block similar to this one:

      In 2006, Jim made a $300 investment in Acme Corp.

      And creating a text block with entity names highlighted:

      [Jim]Person bought 300 shares of [Acme Corp.]Organization in [2006]Time.

      Here, we see the detection and classification of a one-token personal name, a two-token business name, and a temporal expression.

      For English, cutting-edge NER algorithms achieve near-human performance. The best system entering MUC-7, for instance, had an F-measure of 93.39 percent, whereas human annotators achieved F-measures of 97.60 and 96.95 percent, respectively.

      Examples of notable NER systems include:

      GATE is a graphical user interface and Java API for natural language processing that supports NER out of the box for various languages and domains.

      Rule-based and statistical named-entity recognition are both available in OpenNLP.

      SpaCy is a free software named-entity visualizer that also offers quick statistical NER.

      Token categorization using deep learning models is used in Transformers.

      Only entities for which one or more strings, such words or phrases, stands (fairly) consistently for some referent are included in the scope of this work when the word named appears in the expression named entity. Similar to Kripke's rigid designators, but excluding pronouns like it, descriptions that single out a referent by its qualities (see also De dicto and de re), and generic rather than specific noun names (for example Bank).

      Partial named-entity recognition is common, both in theory and practice. In the first stage, names are usually reduced to a segmentation issue in which, for example, Bank of America is treated as a single name despite the fact that the substring America is also a name. This is a chunking-like segmentation issue. The second step is to choose an ontology for classifying entities.

      Expressions in time and certain numbers (e.g, money, percentages, and so on) may be treated as named entities in the NER job.

      Some examples of these kinds serve as excellent illustrations of rigid designators (e.g, several incorrect ones (such 2001 for the year), I take my vacations in June).

      First, let's assume, According to the Gregorian calendar, 2001 was the year of the Millennium.

      The second scenario, June might mean June of any year that isn't specified, next June, every June, etc.).

      It may be argued that, for convenience, the definition of named entity is relaxed in such situations.

      As a result, there is no universally accepted definition of the phrase named entity, and its meaning is usually clarified depending on the surrounding language.

      Named entity types have been suggested to be arranged in a hierarchy. The 29 main categories and 64 subcategories that make up the BBN taxonomy used for question answering were suggested in 2002.

      There are a number of metrics that may be used to assess the performance of a NER system. Precision, recall, and the F1 score are the typical metrics used. There are, however, a number of open questions about how to get at those numbers.

      These statistical methods perform well in the simple circumstances of finding or failing to discover an actual thing precisely, as well as in the more complex cases of finding a non-entity. However, NER might fail in a variety of different ways, some of which can be considered partially accurate and hence disregarded when assessing the success or failure of the technique. For instance, locating a certain thing, but:

      less than ideal token distribution (for example, missing the last token of John Smith, M.D.)

      having an excess of tokens (for example, including the first word of The University of MD)

      Using a variety of methods to divide up neighboring items (for example, treating Smith, Jones Robinson as 2 vs. 3 entities)

      Putting it in the wrong category (for example, calling a personal name an organization)

      classification as a similar but not precise kind (for example, substance vs. drug, or school vs. organization)

      identification of a broad category when the user intended a more specific one (such as recognizing James Madison as a person when it is part of James Madison University). The inability to overlap or nest is a limitation of certain NER systems, necessitating subjective or context-dependent decision-making.

      The percentage of tokens that were appropriately or mistakenly detected as entity references is one simple way to evaluate the quality of an entity recognition system (or as being entities of the correct type).

      There are at least two issues with this:, Most tokens in natural language do not belong to proper names of entities, hence, the default accuracy (constantly forecasting not an entity) is quite high, Usually, 90% or higher; and second, mispredicting the full span of an entity name is not properly penalized (finding only a person's first name when his last name follows might be scored as ½ accuracy).

      The F1 score is defined somewhat differently in academic conferences like CoNLL:

      Number of entity name spans that perfectly match those in the reference dataset (gold standard) is defined as precision.

      I.e.

      when [Person Hans] [Person Blick] is predicted but [Person Hans Blick] was required, The projected name has 0 degree of accuracy.

      After that, we take the mean accuracy across all anticipated entity names.

      The proportion of names from the reference set that were correctly predicted is called recall.

      Harmonic mean (F1) is the average of these two values.

      By definition, a hard mistake is one that does not improve accuracy or recall, such as a forecast that fails to account for a single word, contains a spurious token, or assigns the incorrect class. This is a pessimistic approach since many seeming mistakes may really be very close to being accurate and hence sufficient for certain tasks. For instance, one system may never contain titles like Ms. or Ph.D., but it may be compared to another system or ground-truth data that does. In such circumstances, every occurrence of such a name is considered an oversight. Due to these concerns, it is necessary

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