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Overcategorization
{{unreferenced, date=November 2011 Overcategorization, overcategorisation or category clutter is the process of assigning too many categories, classes or index terms to a given document. It is related to the Library and Information Science (LIS) concepts of document classification and subject indexing. In LIS, the ideal number of terms that should be assigned to classify an item are measured by the variables precision and recall. Assigning few category labels that are most closely related to the content of the item being classified will result in searches that have high precision, I.e., where a high proportion of the results are closely related to the query. Assigning more category labels to each item will reduce the precision of each search, but increase the recall, retrieving more relevant results. Related LIS concepts include exhaustivity of indexing and information overload. Basic principles If too many categories are assigned to a given document, the implications for users ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Overcategorization
{{unreferenced, date=November 2011 Overcategorization, overcategorisation or category clutter is the process of assigning too many categories, classes or index terms to a given document. It is related to the Library and Information Science (LIS) concepts of document classification and subject indexing. In LIS, the ideal number of terms that should be assigned to classify an item are measured by the variables precision and recall. Assigning few category labels that are most closely related to the content of the item being classified will result in searches that have high precision, I.e., where a high proportion of the results are closely related to the query. Assigning more category labels to each item will reduce the precision of each search, but increase the recall, retrieving more relevant results. Related LIS concepts include exhaustivity of indexing and information overload. Basic principles If too many categories are assigned to a given document, the implications for users ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subject Indexing
Subject indexing is the act of describing or classifying a document by index terms, keywords, or other symbols in order to indicate what different documents are ''about'', to summarize their contents or to increase findability. In other words, it is about identifying and describing the '' subject'' of documents. Indexes are constructed, separately, on three distinct levels: terms in a document such as a book; objects in a collection such as a library; and documents (such as books and articles) within a field of knowledge. Subject indexing is used in information retrieval especially to create bibliographic indexes to retrieve documents on a particular subject. Examples of academic indexing services are Zentralblatt MATH, Chemical Abstracts and PubMed. The index terms were mostly assigned by experts but author keywords are also common. The process of indexing begins with any analysis of the subject of the document. The indexer must then identify terms which appropriately identify t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subject Indexing
Subject indexing is the act of describing or classifying a document by index terms, keywords, or other symbols in order to indicate what different documents are ''about'', to summarize their contents or to increase findability. In other words, it is about identifying and describing the '' subject'' of documents. Indexes are constructed, separately, on three distinct levels: terms in a document such as a book; objects in a collection such as a library; and documents (such as books and articles) within a field of knowledge. Subject indexing is used in information retrieval especially to create bibliographic indexes to retrieve documents on a particular subject. Examples of academic indexing services are Zentralblatt MATH, Chemical Abstracts and PubMed. The index terms were mostly assigned by experts but author keywords are also common. The process of indexing begins with any analysis of the subject of the document. The indexer must then identify terms which appropriately identify t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subject Indexing
Subject indexing is the act of describing or classifying a document by index terms, keywords, or other symbols in order to indicate what different documents are ''about'', to summarize their contents or to increase findability. In other words, it is about identifying and describing the '' subject'' of documents. Indexes are constructed, separately, on three distinct levels: terms in a document such as a book; objects in a collection such as a library; and documents (such as books and articles) within a field of knowledge. Subject indexing is used in information retrieval especially to create bibliographic indexes to retrieve documents on a particular subject. Examples of academic indexing services are Zentralblatt MATH, Chemical Abstracts and PubMed. The index terms were mostly assigned by experts but author keywords are also common. The process of indexing begins with any analysis of the subject of the document. The indexer must then identify terms which appropriately identify t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Index Term
In information retrieval, an index term (also known as subject term, subject heading, descriptor, or keyword) is a term that captures the essence of the topic of a document. Index terms make up a controlled vocabulary for use in bibliographic records. They are an integral part of bibliographic control, which is the function by which libraries collect, organize and disseminate documents. They are used as keywords to retrieve documents in an information system, for instance, a catalog or a search engine. A popular form of keywords on the web are tags, which are directly visible and can be assigned by non-experts. Index terms can consist of a word, phrase, or alphanumerical term. They are created by analyzing the document either manually with subject indexing or automatically with automatic indexing or more sophisticated methods of keyword extraction. Index terms can either come from a controlled vocabulary or be freely assigned. Keywords are stored in a search index. Common words li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recall (information Retrieval)
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs (true positives), while the other three are cats (false positives). Seven dogs were missed (false negatives), and seven cats were correctly excluded (true negatives). The program's precision is then 5/8 (true positives / sele ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Overfitting
mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably". An overfitted model is a mathematical model that contains more parameters than can be justified by the data. The essence of overfitting is to have unknowingly extracted some of the residual variation (i.e., the noise) as if that variation represented underlying model structure. Underfitting occurs when a mathematical model cannot adequately capture the underlying structure of the data. An under-fitted model is a model where some parameters or terms that would appear in a correctly specified model are missing. Under-fitting would occur, for example, when fitting a linear model to non-linear data. Such a model will tend to have poor predictive performance. The possibility of over-fitting exists because the criterion used for selecting the model is no ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Subject (documents)
In library and information science documents (such as books, articles and pictures) are classified and searched by subject – as well as by other attributes such as author, genre and document type. This makes "subject" a fundamental term in this field. Library and information specialists assign subject labels to documents to make them findable. There are many ways to do this and in general there is not always consensus about which subject should be assigned to a given document. To optimize subject indexing and searching, we need to have a deeper understanding of what a subject is. The question: "what is to be understood by the statement 'document A belongs to subject category X'?" has been debated in the field for more than 100 years (see below) Theoretical view Charles Ammi Cutter (1837–1903) For Cutter the stability of subjects depends on a social process in which their meaning is stabilized in a name or a designation. A subject "referred ..to those intellections ..that ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Pollution
Information pollution (also referred to as info pollution) is the contamination of information supply with irrelevant, redundant, unsolicited, hampering and low-value information. Examples include misinformation, junk e-mail and media violence. The spread of useless and undesirable information can have a detrimental effect on human activities. It is considered one of the adverse effects of the information revolution. Overview Information pollution generally applies to digital communication, such as e-mail, instant messaging (IM) and social media. The term acquired particular relevance in 2003 when web usability expert Jakob Nielsen published articles discussing the topic. As early as 1971 researchers were expressing doubts about the negative effects of having to recover “valuable nodules from a slurry of garbage in which it is a randomly dispersed minor component.” People use information in order to make decisions and adapt to circumstances. Cognitive studies demonstrate ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Overload
Information overload (also known as infobesity, infoxication, information anxiety, and information explosion) is the difficulty in understanding an issue and effectively making decisions when one has too much information (TMI) about that issue, and is generally associated with the excessive quantity of daily information. The term "information overload" was first used as early as 1962 by scholars in management and information studies, including in Bertram Gross' 1964 book, ''The Managing of Organizations,'' and was further popularized by Alvin Toffler in his bestselling 1970 book ''Future Shock.'' Speier et al. (1999) said that if input exceeds the processing capacity, information overload occurs, which is likely to reduce the quality of the decisions. In a newer definition, Roetzel (2019) focuses on time and resources aspects. He states that when a decision-maker is given many sets of information, such as complexity, amount, and contradiction, the quality of its decision is decre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relevance
Relevance is the concept of one topic being connected to another topic in a way that makes it useful to consider the second topic when considering the first. The concept of relevance is studied in many different fields, including cognitive sciences, logic, and library and information science. Most fundamentally, however, it is studied in epistemology (the theory of knowledge). Different theories of knowledge have different implications for what is considered relevant and these fundamental views have implications for all other fields as well. Definition "Something (A) is relevant to a task (T) if it increases the likelihood of accomplishing the goal (G), which is implied by T." (Hjørland & Sejer Christensen, 2002). A thing might be relevant, a document or a piece of information may be relevant. The basic understanding of relevance does not depend on whether we speak of "things" or "information". For example, the Gandhian principles are of great relevance in today's world. Ep ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Precision (information Retrieval)
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs (true positives), while the other three are cats (false positives). Seven dogs were missed (false negatives), and seven cats were correctly excluded (true negatives). The program's precision is then 5/8 (true positives / sel ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
